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Transit market evaluation of seniors losing driving privileges

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Title:
Transit market evaluation of seniors losing driving privileges
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Book
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English
Creator:
Page, Oliver A
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University of South Florida
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Tampa, Fla
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Subjects / Keywords:
Elderly mobility
Driving cessation
Elderly travel behavior
Transportation policy
Transportation planning
Dissertations, Academic -- Civil Engineering -- Doctoral -- USF
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bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

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Abstract:
ABSTRACT: The projected growth of persons ages 65 and older in the U.S. over the next few decades will usher in an era of unprecedented numbers of seniors licensed to drive. For some members of this group, there will come a time where driving will have to cease due to a variety of factors. At that juncture in their lives, these seniors may have to consider transportation alternatives other than the personally operated vehicle. The objective of this study is to evaluate potential changes in transit market share arising from travel behavior changes of seniors who lose their driving privileges. This includes determining seniors interest in, ability to, and subsequent use of public transit. First, a literature review of developments that have impacted senior travel behavior is presented. Developments such as the changing demographics of seniors, senior socio-economic status, the process of driving retirement, and factors influencing transit use by seniors are presented.^ ^Estimates of the numbers of licensed and former drivers are derived for the year 2030 using several methodological approaches. Trip rates are applied to the predicted non-driving population to derive estimates of the potential demand for transit and subsequent market share. Discussion of the estimated market share results also incorporates a descriptive overview of senior travel behavior as derived from analyses of publicly available datasets followed by focus group results illustrating the experiences of seniors and their transportation choices.Recommendations range from transit agencies engaging in direct "generational" marketing to seniors in order to understand their transportation needs as well as perceptions about transit, promoting the use of transit, and demonstrating the viability of transit for specific trip purposes and partner with rideshare providers.^ ^Despite the predicted increase in transit market shares attributable to the senior population, transit providers have extensive work to do to change the perceptions of transit service provision and subsequently encourage the use of such services by senior populations in forthcoming generations if transit is to become a viable transportation alternative for those seniors ceasing to drive.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2006.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
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by Oliver A. Page.
General Note:
Title from PDF of title page.
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Document formatted into pages; contains 214 pages.
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Includes vita.

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aleph - 001919825
oclc - 185036174
usfldc doi - E14-SFE0001826
usfldc handle - e14.1826
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ABSTRACT: The projected growth of persons ages 65 and older in the U.S. over the next few decades will usher in an era of unprecedented numbers of seniors licensed to drive. For some members of this group, there will come a time where driving will have to cease due to a variety of factors. At that juncture in their lives, these seniors may have to consider transportation alternatives other than the personally operated vehicle. The objective of this study is to evaluate potential changes in transit market share arising from travel behavior changes of seniors who lose their driving privileges. This includes determining seniors interest in, ability to, and subsequent use of public transit. First, a literature review of developments that have impacted senior travel behavior is presented. Developments such as the changing demographics of seniors, senior socio-economic status, the process of driving retirement, and factors influencing transit use by seniors are presented.^ ^Estimates of the numbers of licensed and former drivers are derived for the year 2030 using several methodological approaches. Trip rates are applied to the predicted non-driving population to derive estimates of the potential demand for transit and subsequent market share. Discussion of the estimated market share results also incorporates a descriptive overview of senior travel behavior as derived from analyses of publicly available datasets followed by focus group results illustrating the experiences of seniors and their transportation choices.Recommendations range from transit agencies engaging in direct "generational" marketing to seniors in order to understand their transportation needs as well as perceptions about transit, promoting the use of transit, and demonstrating the viability of transit for specific trip purposes and partner with rideshare providers.^ ^Despite the predicted increase in transit market shares attributable to the senior population, transit providers have extensive work to do to change the perceptions of transit service provision and subsequently encourage the use of such services by senior populations in forthcoming generations if transit is to become a viable transportation alternative for those seniors ceasing to drive.
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Transit Market Evaluation of Seniors Losing Driving Privileges by Oliver A. Page A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Civil & Environmental Engineering College of Engineering University of South Florida Co-Major Professor: Steven Polzin, Ph.D. Co-Major Professor: Ram Pendyala, Ph.D. Jian “John” Lu, Ph.D. Edward Mierzejewski, Ph.D. Brent Small, Ph.D. James Stock, Ph.D. Beverly Ward, Ph.D. Date of Approval: November 2, 2006 Keywords: Elderly Mobility, Driving Cessation, Elderly Travel Behavior, Transportation Policy, Transportation Planning Copyright 2006, Oliver A. Page

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DEDICATION This research accomplishment is dedicated to three women in my life: my mother, Jlona; my sister, Belle; and my aunt, Myrtle, who, for their prayers, copious words of encouragement and support throughout my academic career have enabled me to reach this milestone in my life. It is also dedicated to the those individuals who in their academic careers keep on trying against all odds, recognizing that through persistence and prayers you can reach the “stature of the fu llness of Christ.” To my family members, relatives and friends, at last I can say that, “I have crossed the final academic finish line, thank you for your prayers and cheering me on, even when at times all I did was study, study, study!!!”

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ACKNOWLEDGEMENTS I am indebted to many people who have enabled me to reach this academic milestone in my life. Thanks go to Dr. Ram Pendyala for offering the scholarship opportunity to study at the University of South Florida as well as being a co-chairman on my dissertation committee: Dr. Steven Polzin, as co-chairman, whose brilliant wit, professional guidance as my project manager, and insight in transportation issues has contributed to my continued growth both professionally and academic ally: Dr. Edward Mierzejewski for his leadership at Center for Urban Transportation Research, a research institution that has afforded me unique learning opportunities; Dr. Brent Small, who exposed me to the fascinating field of gerontology and Drs. James Stock and Jian “John” Lu for being members of my dissertation committee and providing invaluable feedback through the dissertation process. Special thanks go to Dr. Beverly Ward for her friendship and mentorship (by way of being her teaching assistant) and for always being on call to answer my many questions about life in America. Also worthy of mention are Dr. Peter Freeman for his mentorship, honest opinion and friendship over many years: Dr. Xuehao Chu, whose cunning insight on a variety of issues I have always appreciated; Nanda Srinivasan and Nancy McGuckin for clarifying issues relating to the National Household Travel Survey; the Transit and Research Units of the Florida Department of Transportation for providing a research platform that sparked my interest in senior mobility issues and the Southeastern Transportation Center for providing funding throughout my time as research assistant on the doctoral

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program. Sincere thanks also go to the seniors who participated in the focus group discussions for sharing their traveling experiences and challenges before and after driving cessation. Last, I could not have achieved this academic milestone without the input from Jon Burkhardt, Drs. Brigitte Waldorf, Daniel Foley, Richard Wallace, John Eberhard, and Lydia Kostynuik, experts in the field of seni or mobility, some of whom I have never met; nevertheless, they always answered my emails seeking clarification of issues facing seniors in meeting their transportation needs, and commented on my interpretation of their published works. To the many others, too many to name, who have played some role in making this doctoral research effort possible, again, I say thank you.

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i TABLE OF CONTENTS LIST OF TABLES.............................................................................................................vi LIST OF FIGURES...........................................................................................................xi ABSTRACT.....................................................................................................................xi ii CHAPTER 1 – INTRODUCTION......................................................................................1 1.1 Context...............................................................................................................1 1.2 Study Objective...................................................................................................2 1.3 Background.........................................................................................................3 1.4 Scope of Study...................................................................................................6 1.5 Report Structure.................................................................................................6 CHAPTER 2 – RECENT DEVELOPMENTS IMPACTING SENIOR TRAVEL BEHAVIOR ................................................................................8 2.1 Introduction.........................................................................................................8 2.2 Older Drivers and Driving Cessation...................................................................8 2.3 The Driving Cessation Process.........................................................................10 2.4 Seniors Who Retire from Driving......................................................................11 2.5 Driving Cessation Other Factors.....................................................................12 2.5.1 Gradual and Planned Cessation...................................................................12 2.5.2 Sudden and Unplanned Cessation...............................................................15 2.6 Demographics...................................................................................................16 2.7 Closing Gap of Licensure Rates by Gender.....................................................18 2.8 Moves Toward Age-Based Driver Testing by State Licensing Authorities........19 2.9 Seniors Potential to be More Dependent on Outside Resources for Mobility......................................................................................23 2.10 Household Composition and Driving Cessation................................................26 2.11 Trends Influencing Senior Transit Use or Non-use...........................................27 2.12 Trends Having Positive Potential on Senior Transit Use..................................28 2.12.1 Minority Elders...........................................................................................28 2.12.2 Number of Adult Children..........................................................................28 2.12.3 Physical Distance of Adult Children...........................................................30 2.12.4 Marital Status of Elderly Population...........................................................32 2.12.5 Technology and Design.............................................................................33 2.13 Factors Having Negative Potential on Senior Transit Use................................34 2.13.1 Life Expectancy and Health.......................................................................34 2.13.2 Aging in the Suburbs.................................................................................36 2.13.3 Technology................................................................................................37 2.14 Transit Service Provision Planning...................................................................38 i

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i CHAPTER 3 – DATA AND METHODS...........................................................................41 3.1 Introduction.......................................................................................................41 3.2 Methodological Approach.................................................................................42 3.3 Quantitative Methodology and Primary Data Sources......................................43 3.3.1 National Household Travel Survey................................................................43 3.3.2 Decennial Census.........................................................................................43 3.4 Quantitative Data Sources (Secondary)...........................................................44 3.4.1 Health and Retirement Study........................................................................44 3.5 National Versus State Level Analysis...............................................................44 3.6 Base Year.........................................................................................................45 3.7 Future Year.......................................................................................................45 3.8 Driver Licensing Rates and Numbers of Drivers...............................................46 3.9 Driving Cessation..............................................................................................52 3.9.1 Estimates of Driving Cessation Wallace.....................................................53 3.9.2 Estimates of Driving Cessation – Foley et al.................................................54 3.9.3 Estimates of Driving Cessation – Waldorf.....................................................56 3.9.4 Estimates of Driving Cessation – Waldorf and Pitfield..................................59 3.10 Driving Cessation Caveats...............................................................................67 3.10.1 Cumulative Cessation Rates Over Time....................................................68 3.10.2 Gender Differences in Cessation Rates....................................................68 3.10.3 Cessation Rates of the 65 to 69 Year Cohort............................................69 3.11 Derivation of Potential Transit Market Size.......................................................69 3.12 Travel Behavior of the Elderly...........................................................................70 3.13 Dataset Caveats...............................................................................................71 3.13.1 Cross Sectional Versus Longitudinal Datasets..........................................71 3.13.2 Institutionalized Populations......................................................................72 3.13.3 Transportation Definitions..........................................................................72 3.13.4 Driving/Licensure Status............................................................................73 3.13.5 Elderly Demographics...............................................................................74 3.14 Transit Trip Rates.............................................................................................75 3.14.1 Transit Trip Rates – Market Assessment #1: General Population....................................................................................75 3.14.2 Transit Trip Rates – Market Assessment #2: Urban/Rural Population.............................................................................75 3.14.3 Transit Trip Rates – Market Assessment #3: Urban Driver/Non-Driver Population..........................................................77 3.14.4 Transit Trip Rates – Market Assessment #4 Urban Non-Driving Population and Household Driver Availability.............77 3.14.5 Transit Trip Rates – Market Assessment #5 Urban Non-Driving Population and Household Vehicle Availability...........77 3.14.6 Summary Trip Frequency Behavior...........................................................78 CHAPTER 4 – RESULTS AND DISCUSSION...............................................................79 4.1 Introduction.......................................................................................................79 4.2 Estimates of the Senior Driving Population in 2030..........................................79 4.3 Active and Former Drivers................................................................................80 4.4 Never Driven.....................................................................................................85 4.5 Driving Cessation Favorable Versus Worse Case Analysis.............................86 ii

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i 4.6 Transit Use by Seniors Evidence from the National Household Travel Survey 2001...........................................................................................89 4.6.1 Gender and Transit Use................................................................................90 4.6.2 Minorities and Transit Use............................................................................93 4.6.3 Modal/Market Share by Age Cohort..............................................................94 4.6.4 Transit Trip Starting Time..............................................................................97 4.6.5 Transit Trip Starting Time by Age Cohort......................................................98 4.6.6 Transit Trips and Trip Purpose......................................................................99 4.6.7 Transit Trips by Day of Week......................................................................102 4.6.8 Transit Trip Distance...................................................................................103 4.6.9 Transit Trip Travel Time..............................................................................104 4.6.10 Transit Travel and Medical Condition......................................................105 4.7 Households and Senior Households..............................................................106 4.7.1 Household Population Caveat.....................................................................107 4.7.2 Driver or Vehicle Availability by Household Size.........................................109 4.7.3 Households and Seniors Only Households.................................................111 4.8 Trip Frequency Behavior................................................................................112 4.9 Transit Market Share Results – Market Assessment #1.................................113 4.10 Transit Market Share Results – Market Assessment #2.................................114 4.11 Transit Market Share Results – Market Assessment #3.................................117 4.12 Transit Market Share Results – Market Assessment #4.................................119 4.13 Transit Market Share Results – Market Assessment #5.................................122 4.14 Market Share Sensitivity Analyses..................................................................125 4.15 Market Assessment Summary........................................................................127 4.16 Seniors Perceptions and Experiences with Transit.........................................127 4.16.1 Focus Group Methodology......................................................................128 4.16.2 Factors Initiating Use of Public Transportation........................................129 4.16.3 Concerns about Using Public Transit......................................................132 4.16.4 Viability of Using Public Transit in the Future..........................................135 CHAPTER 5 – CONCLUSIONS AND RECOMMENDATIONS.....................................137 5.1 Introduction.....................................................................................................137 5.2 Transit Market Size.........................................................................................137 5.3 Driving Transition and Subsequent Transportation Options...........................138 5.4 Migration and Seniors.....................................................................................140 5.5 Senior Conducive Transportation Environments............................................141 5.6 Working Seniors.............................................................................................142 5.7 Meeting Transportation Needs Through Public Versus Private Provision......143 5.8 Implications for Senior Mobility Providers.......................................................144 5.8.1 Financial......................................................................................................144 5.8.2 Operations...................................................................................................145 5.8.3 Infrastructure...............................................................................................146 5.9 The Next Steps...............................................................................................146 5.10 Study Limitations............................................................................................148 5.11 Future Research Needs..................................................................................149 5.12 Recommendations..........................................................................................151 5.13 Conclusions....................................................................................................153 iii

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i REFERENCES..............................................................................................................155 APPENDICES ...............................................................................................................165 Appendix A List of Acronyms....................................................................................166 Appendix B Driving Cessation Estimates for Older Males and Females Waldorf (2001)........................................................................168 Appendix C Complete Life Tables 2000....................................................................170 Appendix D Calculation of Life Tables for Persons Ages 35 and Older (Base Year 2000)..................................................................174 Appendix E Calculation of Survivor Curves xSand xS*for Persons Ages 35 and Older (Base Year 2000) .................................................................179 Appendix F Recalculation of Licensing Proportions for Persons Ages 85 and Older in 2030 (Base Year 2000).....................................................186 Appendix G Population Estimates (2001) Derived from the NHTS Person and Household Files..............................................................................189 Appendix H Transit Market Share Assessments – Detailed Calculations.................190 Appendix J Transit Market Share Assessments – Sensitivity Tests.........................199 Appendix K Focus Group Questionnaire – Current Drivers......................................208 Appendix L Focus Group Questionnaire – Former/Non-Drivers...............................211 ABOUT THE AUTHOR.......................................................................................End Page iv

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i LIST OF TABLES Table 2.1 Daily Trip Frequency According to Household Size................................32 Table 2.2 Life-, Activeand Driving-Life Expectancies by Age................................35 Table 3.1 Dataset Year of Survey............................................................................45 Table 3.2 2030 Population Estimates by Cohort (in ‘000s)......................................46 Table 3.3 Licensing Proportions (Males) in 2000....................................................47 Table 3.4 Licensing Proportions (Females) in 2000................................................48 Table 3.5 Predicted Number of License Holders (Males) in 2030...........................48 Table 3.6 Predicted Number of License Holders (Females) in 2030.......................48 Table 3.7 Licensure Rates and Cohort Projections.................................................49 Table 3.8 Comparison of Predicted Number of Licensed Holders (Males) in 2030...........................................................................51 Table 3.9 Comparison of Predicted Number of License Holders (Females) in 2030......................................................................51 Table 3.10 Numbers of License Holders (Males) in 2000 and 2030 .........................52 Table 3.11 Numbers of License Holders (Females) in 2000 and 2030.....................52 Table 3.12 Proportion of Senior Former Drivers (Percent Stopped Driving).............53 Table 3.13 Predicted Drivers and Former Drivers 2030 (Based on Wallace)............55 Table 3.14 Prevalence of Driving and Not Driving for Males (1993 – 1995)..............56 Table 3.15 Prevalence of Driving and Not Driving for Females (1993 – 1995).........56 Table 3.16 Predicted Drivers and Former Drivers 2030 (Based on Foley et al.).......57 Table 3.17 Driving Cessation Estimates for Older Persons in the USA (Males).......59 Table 3.18 Driving Cessation Estimates for Older Persons in the USA (Females)...59 v

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ii Table 3.19 Predicted Drivers and Former Drivers 2030 (Based on Waldorf)............60 Table 3.20 Life Table for Males: United States 35yrs+ in 2000.................................62 Table 3.21 Life Table for Females: United States 35yrs+ in 2000.............................62 Table 3.22 Male and Females Survivor Probabilities xS (65 Years and Older)........63 Table 3.23 Five-Year (Assumed) Cessation Probabilities for Seniors.......................64 Table 3.24 Male and Female Survivor (xS) and Surviving & Driving (xS*) Probabilities.............................................................................................64 Table 3.25 Estimated Former Driver Population in 2030 (Males)..............................67 Table 3.26 Estimated Former Driver Population in 2030 (Females)..........................67 Table 3.27 National Household Travel Survey 2001 Data File Statistics..................71 Table 3.28 Transit Trip Rates Market Assessment #1: General Population..................................................................................75 Table 3.29 Transit Trip Rates Market Assessment #2: Urban/Rural Population...........................................................................76 Table 3.30 Transit Trip Rates Market Assessment #3: Urban Driver/Non-Driver Population........................................................76 Table 3.31 Transit Trip Rates Market Assessment #4: Urban Non-Driving Population and Household Driver Availability..........................................76 Table 3.32 Transit Trip Rates Market Assessment #5: Urban Non-Driving Population and Household Vehicle Availability........................................76 Table 4.1 Estimated Senior Population by Licensure Status (Males) 2030.............80 Table 4.2 Estimated Senior Population by Licensure Status (Females) 2030.........80 Table 4.3 Estimated Senior Former Driver Population 2030...................................81 Table 4.4 Estimated Senior Former Driver Population 2030 (Favorable Case – Foley et al., 2002).....................................................87 Table 4.5 Estimated Senior Former Driver Population 2030 (Worse Case – Waldorf, 2001)................................................................88 Table 4.6 Daily Travel by Mode (Billion Trips in 2001) in U.S.A..............................90 vi

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iii Table 4.7 Modal Split for Daily Travel in U.S.A. (Billion of Trips Males Year 2001)....................................................................................91 Table 4.8 Modal Split for Daily Travel in U.S.A. (Billion of Trips Females Year 2001)................................................................................92 Table 4.9 Distribution of Trip by Trip Purpose, Travel Mode and Age Cohort.......100 Table 4.10 Trip Distance Statistics NHTS 2001......................................................103 Table 4.11 POV Trip Distance Statistics NHTS 2001..............................................103 Table 4.12 Transit Trip Distance Statistics NHTS 2001..........................................104 Table 4.13 Trip Travel Time Statistics NHTS 2001.................................................104 Table 4.14 POV Travel Time Statistics NHTS 2001................................................105 Table 4.15 Transit Travel Time Statistics NHTS 2001.............................................105 Table 4.16 Household Size and Senior Members...................................................107 Table 4.17 Senior Population According to Household Size...................................108 Table 4.18 Households and Senior Households Vehicle Availability.......................109 Table 4.19 Households and Senior Households Driver Availability.........................110 Table 4.20 Senior Population in Zero Vehicles or Zero Drivers Available Households............................................................................111 Table 4.21 Household Size Where All Members are Seniors..................................111 Table 4.22 Daily Average Number of Trips..............................................................112 Table 4.23 Daily Average Number of Trips by Gender and Age Cohort..................112 Table 4.24 Senior Transit Market Share Assessment #1........................................114 Table 4.25 Senior Transit Market Share Assessment #2 (Year 2001)....................116 Table 4.26 Senior Transit Market Share Assessment #2 (Year 2030)....................116 Table 4.27 Senior Transit Market Share Assessment #3 (Year 2001)....................118 Table 4.28 Senior Transit Market Share Assessment #3 (Year 2030)....................119 Table 4.29 Urban Non-Driver Respondent According to Household Driver Availability (Year 2001)...............................................................120 vii

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iv Table 4.30 Urban Non-Drivers According to Household Driver Availability (Year 2030)............................................................................................121 Table 4.31 Senior Transit Market Share Assessment #4 (Year 2001)....................122 Table 4.32 Senior Transit Market Share Assessment #4 (Year 2030)....................122 Table 4.33 Urban Non-Driver Respondent According to Household Vehicle Availability (Year 2001).............................................................123 Table 4.34 Urban Non-Drivers According to Household Vehicle Availability (Year 2030)..........................................................................124 Table 4.35 Senior Transit Market Share Assessment #5 (Year 2001)....................124 Table 4.36 Senior Transit Market Share Assessment #5 (Year 2030)....................125 Table 4.37 Senior Transit Market Share Assessment #4 Gender Licensing Equal (Year 2030)....................................................126 Table 4.38 Senior Transit Market Share Assessment #4 Gender Cessation Rates Equal (Year 2030).........................................126 Table 4.39 Senior Transit Market Share Assessment #4 Gender Licensing and Cessation Rates Equals (Year 2030).............................126 Table 4.40 Overall Market Assessment Results......................................................127 Table 4.41 Viability of Future Consideration of Public Transportation as a Transportation Alternative......................................................................135 Table A.1 List of Acronyms....................................................................................166 Table B.1 Driving Cessation Estimates (Males) Waldorf (2001)............................168 Table B.2 Driving Cessation Estimates (Females) Waldorf (2001)........................169 Table C.1 Life Table for Males: United States, 2000..............................................170 Table C.2 Life Table for Females: United States, 2000.........................................172 Table D.1 Male and Female Death Rates Year 2000............................................175 Table D.2 Male and Female Probabilities of Dying (xq) Year 2000.......................176 Table D.3 Life Table for Males: United States 35yrs+, 2000..................................178 Table D.4 Life Table for Females: United States 35yrs+, 2000.............................179 viii

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v Table E.1 Abridged Life Table for Males: United States 35yrs+, 2000..................180 Table E.2 Abridged Life Table for Females: United States 35yrs+, 2000..............181 Table E.3 Survival Probabilities xSfor Males: United States 35yrs+, 2030...................................................................182 Table E.4 Survival Probabilities xSfor Females: United States 35yrs+, 2030...................................................................182 Table E.5 Revised Male Population at Risk of Dyingxl: United States 35yrs+, 2000..........................................................................................183 Table E.6 Revised Female Population at Risk of Dyingxl: United States 35yrs+, 2000..........................................................................................184 Table E.7 Survival Probabilities xS* for Males: United States 35yrs+, 2030...................................................................184 Table E.8 Survival Probabilities xS* for Females: United States 35yrs+, 2030...................................................................185 Table F.1 Population and Licensing Statistics for the 85year+ Cohort..................186 Table F.2 Licensing Proportions of Senior Males Ages 55+ years in 2000.........................................................................187 Table F.3 Licensing Proportions of Senior Females Ages 55+ years in 2000.........................................................................187 Table F.4 Licensing Proportions of Senior Males Ages 85+ years in 2030.........................................................................187 Table F.5 Licensing Proportions of Senior Females Ages 85+ years in 2030.........................................................................188 Table G.1 NHTS Population Estimates (2001)......................................................189 Table H.1 Market Assessment #1..........................................................................190 Table H.2 Market Assessment #2..........................................................................191 Table H.3 Market Assessment #3 .........................................................................192 Table H.4 Market Assessment #3 Senior Active, Former and Non-Drivers...........193 Table H.5 Market Assessment #3 2030 Population Estimates..............................194 ix

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vi Table H.6 Market Assessment #4 Urban Non-Driving Seniors According to Household Driver Availability Status.................................195 Table H.7 Market Assessment #4 2030 Population Estimates..............................196 Table H.8 Market Assessment #5 Urban Non-Driving Seniors According to Household Vehicle Availability Status...............................197 Table H.9 Market Assessment #5 2030 Population Estimates..............................198 Table J.1 Market Assessment #4 (Sensitivity Test #1 – Trip Rates Male & Female Licensing Proportions Equal)........................................199 Table J.2 Market Assessment #4 (Sensitivity Test #1 – Male & Female Licensing Proportions Equal)........................................200 Table J.3 Market Assessment #4 (Sensitivity Test #1 – Drivers Male & Female Licensing Proportions Equal)...........................201 Table J.4 Market Assessment #4 (Sensitivity Test #2 – Trip Rates Male & Female Cessation Rates Equal)................................................202 Table J.5 Market Assessment #4 (Sensitivity Test #2 – Male & Female Cessation Rates Equal).........................................................................203 Table J.6 Market Assessment #4 (Sensitivity Test #2 – Drivers Male & Female Cessation Rates Equal)...................................204 Table J.7 Market Assessment #4 (Sensitivity Test #3 – Trip Rates Male & Female Licensing and Cessation Rates Equal).........................205 Table J.8 Market Assessment #4 (Sensitivity Test #3 Male & Female Licensing and Cessation Rates Equal)..................................................206 Table J.9 Market Assessment #4 (Sensitivity Test #3 Drivers Male & Female Licensing and Cessation Rates Equal)............207 x

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i LIST OF FIGURES Figure 2.1 The Process of Driving Cessation...........................................................11 Figure 2.2 United States Population 2000................................................................17 Figure 2.3 Projected United States Population 2030................................................17 Figure 2.4 Percentage of Persons 65 Years or More of Total Population (2000 – 2030)........................................................................18 Figure 2.5 Percentage of Population Licensed by Age Cohort and Gender in 2000........................................................................................19 Figure 2.6 Living Arrangements of Adults 65 Years and Older 1990 and 2000...............................................................................24 Figure 2.7 Persons Who Needed Assistance with Activities by Age........................25 Figure 2.8 Fertility Rates and Parent Support Ratios 1960 to 2030.........................30 Figure 3.1 Senior Population Cohorts of Total Population 2000 – 2050...................47 Figure 3.2 Survivor Curves xS and xS* (Males)......................................................65 Figure 3.3 Survivor Curves xS and xS* (Females)..................................................65 Figure 3.4 Percent of Senior Population Residing in Institutions 2000.....................73 Figure 3.5 Population Estimates by Senior Age Cohort (Year 2001)........................74 Figure 3.6 Senior Population Daily Trip Rate Tree...................................................78 Figure 4.1 Estimated Male Former Drivers 2030......................................................82 Figure 4.2 Estimated Female Former Drivers 2030..................................................82 Figure 4.3 Estimated Current and Future Senior Drivers According to Driving Status (70 Years and Older) ...................................................86 Figure 4.4 Percent Transit Trip by Senior Age Cohort (Year 2001)..........................91 xi

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ii Figure 4.5 Percent Transit Trips by Senior Age Cohort and Gender (Year 2001)....92 Figure 4.6 Percent Transit Trips by Age Cohort and Ethnicity (Year 2001)..............93 Figure 4.7 Mode/Market Share by Age Cohort.........................................................95 Figure 4.8 Urban and Rural Mode Share by Age Cohort..........................................96 Figure 4.9 Distribution of Transit Trips by Start Hour................................................98 Figure 4.10 Transit Trip Starting Time by Age Cohort................................................99 Figure 4.11 Distribution of POV, Transit and Walk Trips by Seniors by Start Hour..101 Figure 4.12 Daily Distribution of Transportation Mode Use by Seniors....................102 Figure 4.13 Medical Condition Im pacting Out-of-Home Mobility...............................106 Figure 4.14 Factors Enhancing Potential Use of Public Transportation...................132 Figure 4.15 Factors Influencing Concerns About Using Public Transit.....................133 Figure 5.1 Post Cessation Transportation Options.................................................139 xii

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iii TRANSIT MARKET EVALUATION OF SENIORS LOSING DRIVING PRIVILEGES Oliver A. Page ABSTRACT The projected growth of persons ages 65 and older in the U.S. over the next few decades will usher in an era of unprecedented num bers of seniors licensed to drive. For some members of this group, there will come a time where driving will have to cease due to a variety of factors. At that juncture in their lives, these seniors may have to consider transportation alternatives other than the pers onally operated vehicle. The objective of this study is to evaluate potential changes in transit market share arising from travel behavior changes of seniors who lose their dr iving privileges. This includes determining seniors interest in, ability to, and subsequent use of public transit. First, a literature review of developments that have impacted senior travel behavior is presented. Developments such as the changing demographics of seniors, senior socio-economic status, the process of driving retirement, and factors influencing transit use by seniors are presented. Estimates of the numbers of licensed and former drivers are derived for the year 2030 using several methodological approaches. Trip rates are applied to the predicted non-driving population to derive estimates of the potential demand for transit and subsequent market share. Discussion of the estimated market share results also incorporates a descriptive overview of senior travel behavior as derived from analyses of publicly availabl e datasets followed by focus group results illustrating the experiences of seniors and their transportation choices. xiii

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iv Recommendations range from transit agencies engaging in direct “generational” marketing to seniors in order to understand their transportation needs as well as perceptions about transit, promoting the use of transit, and demonstrating the viability of transit for specific trip purposes and partner with rideshare providers. Despite the predicted increase in transit market shares attributable to the senior population, transit providers have extensive work to do to change the perceptions of transit service provision and subsequently encourage the use of such services by senior populations in forthcoming generations if transit is to become a viable transportation alternative for those seniors ceasing to drive. xiv

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1 CHAPTER 1 – INTRODUCTION 1.1 Context Effective January 1, 2004, Florida Statute 322.18, subsection 5, required drivers 79 years or older to pass vision tests when r enewing their six-year licenses. Such a mandate is part of an “age-based” testing regime that several U.S. states have implemented in recent years with respect to enhancing the safety environment afforded to road users.1 Age-based license renewal and testing is defined as a situation where “the nature or schedule of renewal testing changes with age” (Lange & McKnight 1996, p. 81). This action is one of many taking place that signal recognition of the impending boom in population that will reach age bra ckets where driving risks are known to increase. Perhaps more so than prior generations, the next generation of elders are individuals who, for the most part, have a long history of driving; are independentminded; have grown accustomed to high levels of mobility, which they cherish; and are less likely to have spouses, siblings, and children who are able to provide for their mobility. Thus, the role of government in regulating driver licensing and in providing mobility alternatives promises to be a challenge over the next several decades. Implementation of “age-based testing” will produce a group of travelers who could serve as a resource in understandi ng travel behavior changes and mode choice after driving cessation. A richer understanding of driving cessation and accommodation will enable informed planning and policy making to support the mobility of non-drivers in 1 For the current status of senior licensing laws in the U.S. refer to the AAA Public Affairs website (www.aaapublicaffairs.com).

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2 their communities, as “the [transportation] needs of older citizens are predictable so accommodating them is possible” (Freund, 2004, p. 114). Nevertheless, the wider implications of this potential challenge need to be placed against the backdrop that “mobility is critical to well-being” (Coughlin & Lacombe 1997, p. 91). 1.2 Study Objective With an aging population, it is very important to understand older adult travel needs and behaviors, particularly at a point in time when they are no longer able to drive. The objective of this study is to evaluate potential changes in transit market share arising from travel behavior changes of seniors who lose their driving privileges, particularly their interest in, ability to, and subsequent use of public transit. In other words, to what extent could this group of seniors meet their transportation needs through the use of transit services, potentially contributing to transit market share? The public transportation industry has shown a keen interest in the challenges and opportunities that can be presented as the baby boomers age (i.e., persons born between 1946 and 1965) and perhaps cease driving. With the predicted growth of new retirees expected over the next few years, some of them will lose their ability to drive, creating an opportunity for the public transpor tation community to provide a valuable service for these individuals. Within the industry, there is a range of expectations with respect to the size and opportunity this market may present to public transportation. While some feel there is an impending tidal wave of opportunity and need, others are more sanguine, reflecting on the prospect that few of the baby boomers have ever used public transportation; more are attached to auto mobility; and fewer live in areas sufficiently dense to support quality public transit service.

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3 While it is premature to determine the magnitude of the role that public transportation might play in meeting the travel needs of elder baby boomers, it is certainly reasonable to anticipate that public transportation will be an important provider for some segments of the population. There will be individuals who will lose their driving privileges and will not have alternative mobility options that might be afforded through privately-purchased services or strong family support. Public transportation will be called on to provide a safety net for this segment of the population. Thus, it is prudent for the public transportation research community to begin to explore the nature of the travel demand that may arise and how the industry might position itself to respond. Towards that end, this research effort can make a useful contribution. 1.3 Background At a time when 85 percent of persons over the age of 15 years hold a driver’s license (Office of Highway Policy Information [OHPI], 2005), and each person in 2001 traveled on average 40 miles per day, of which 35 m iles were in a personal vehicle (U.S Department of Transportation 2003, p. 9), mobility has reached unprecedented levels. This is coupled with seniors experiencing “longer, happier, fuller lives than their counterparts today and certainly than the elderly of just a few decades ago” (Rosenbloom 2004, p. 3). The ability to drive, is for many people, highly correlated to their level of enjoyment of life. Using the 1995 Nationwide Personal Travel Survey (NPTS) data, Evans (2001, p. 152), found that there was a substantial difference in tripmaking associated with driving and that this association increases with age. This difference is most pronounced and most critical among the 75+ population. While 75 percent of 75+ drivers went out at least once on their trip day, just 44 percent of nondrivers ages 75 and older went out. This finding suggests that having access to a car

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4 allows greater participation in activities outside of the home, and thus elevates ones enjoyment of life and well-being. A similar result was found by Straight (1997) in her study of travel behavior and preferences of drivers and non-drivers 75 y ears and older where she concluded that the “level of mobility is strongly related to whether or not one drives.” However, caution needs to be exercised here, as some of the considerations that influence driving also influence the desire for mobility. For example, persons with serious mobility limitations such as being bedridden have constraints to mobility beyond their ability to drive a vehicle. Rosenbloom (2004, p. 3), while reflecting on the potential rosy outlook, goes on to state that: …… there is no evidence that older people’s desire to travel will decline at the same rate as their ability to drive or to find other [mobility] options. Many older people may ultimately find themselves cut off from the very aspects of life that made their early retirement years so much better than those of older people only a few decades ago. Along with the inability to drive and its impact on mobility, “declining health may well result in reduced activity regardless of the ability to drive” (Marottoli et al. 2000, p. S335). Thus, it can be argued that there are at least two generalized mobility challenges faced by the elderly: the means of mobility, e. g., personal transportation; and the physical capacity to be mobile, influenced by the physical/health status of the individual. Being able to operate a car has become synonymous (and, in many cases, a necessary requirement) to experience enhanced levels of livability and consumption. Foley et al. (2002, p. 1288) describe the operation of a car as a “pervasive task of

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5 independence.” The intimate relationship between man and the automobile has resulted in a situation where, “for most Americans, driving is considered essential to personal well-being” (Adler et al. 1999, p. 28) and “essential to maintain a good quality of life” (Adler et al. 2000, p. 40). This dependence on driving has created a situation where giving it up may be experienced “as the first step towards a downward spiral of dependency” (Horowitz et al. 2002, p. 262). This state of dependency becomes critical when no family member or friend is available nearby to assist the individual, which may lead to isolation, eating disorders, institutionalization, and premature death (McSwain, as quoted in Stanfield [1996]). Because of dependency on a lifestyle that has revolved around the capacity to drive an automobile, any changes brought about by transitions in personal mobility will have far reaching consequences, impacting not only the individuals involved, but their immediate families and society as well. The uniqueness of the U.S. mobility environment has given rise to the above situation as “in many areas of the country there is no adequate public transportation, and many people must drive if they are to function in their community” (Freedman & Freedman 1996, p. 876). Indeed, “recent and contemporary urban development practices and public transportation policies have catapulted the private car into its role as the preeminent means of individual transportation” (Yassuda et al. 1997, p.525). This has resulted in negative and yet unwarranted perceptions of public transportation to be held by many people. Studies have related how the elderly view public transportation in the U.S. as inconvenient, unpleasant, and even dangerous if it requires waiting at secluded bus stops or crossing busy intersections (Messinger-Rapport & Rader 2000). Noting these negative perceptions of public transportation, the elderly may feel that, after driving for many years, “they deserve better” (Shope 2003, p. 58).

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6 The myriad factors that can influence driving ability and the onset of driving cessation have given rise to the need for a greater understanding of travel behavior during and after this period of transition. Adler et al. (1999) surmised that the longer an individual drives, the more accustomed they become towards driving and the less likely they are to cease from driving even after di agnosis (of a condition that affects driving ability) and the greater risk they become to ot her road users. Mobility providers and, of particular interest in this research, public transportation operators can benefit from a better understanding of the potential size and mobility needs of the market of individuals who may be ceasing to drive. 1.4 Scope of Study The focus of this study is mobility issues pertinent to the senior population in the U.S. Literature resources referenced in this project are based on studies conducted in the U.S., as published from January 1990 to September 2006. A variety of electronic databases related to aging/gerontology and transportation were searched, e.g., Ageline, PsyInfo, and Transportation Research Information Services (TRIS) to ascertain the extent and depth of prior research into senior mobility. The key search strings were “driving cessation” and “cessation of driving.” The reader is referred to a publication entitled “Age-Related Disabilities That May Impair Driving and Their Assessment,” which provides an exhaustive literature review by Janke (1994) or to search the above-named databases for further references. 1.5 Report Structure This study is presented in five chapters. This first chapter provides an introduction and context setting of the study. Chapter 2 represents a literature review and findings from focus group discussions that have impacted s enior travel behavior. Developments such

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7 as the changing demographics of seniors, senior socio-economic status, the process of driving retirement (i.e., driving reduction ulti mately resulting in driving cessation) and factors influencing transit use by seniors are presented are reviewed. Chapter 3, details the data used and methodology applied in developing the estimate of the potential senior transit market developed in this research project. Estimates of licensed and former drivers in the forecast year of 2030 are obt ained. This is followed by a descriptive overview of senior travel behavior as der ived from analyses of publicly available datasets. The application of trip rates (i.e., daily propensity to travel) to the forecast senior population produces an estimate of the size of the senior transit market, which is presented in Chapter 4. Chapter 4 continues with a discussion of the results through the creation of various hypothetical scenarios with respect to senior mobility as well as focus group results illustrating the experiences of seniors and their transportation choices. Finally, conclusions and recommendations em anating from this research project are presented in Chapter 5. Note: In this report the terms “seniors” and the “elderly” are used interchangeably, generally referring to persons 55 years of age and older. In addition, persons between 55 64 years of age are also referred to as the “young-old,” 65 74 years the “old-old,” and those 75 years and older the “oldest-old.”

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8 CHAPTER 2 – RECENT DEVELOPMENTS IMPACTING SENIOR TRAVEL BEHAVIOR 2.1 Introduction Knowledge of the phenomenon of driving cessation will provide valuable insight into travel and transit use by the growing population of older Americans. Indeed, “the challenge to understand personal lifestyle and transportation decision-making as people age” (Transportation Research Board [TRB] 2005, p. 24) still represents an increasingly critical research need in 2006. In the process of striving to develop a richer understanding of driving cessation, determi ning its impact on subsequent travel behavior, and understanding the viability of public transit use in maintaining senior mobility and well-being, a number of recent developments influence our thinking. These developments are presented in this chapter. 2.2 Older Drivers and Driving Cessation Kostyniuk & Shope (2003, p. 408) remark that “there is no precise age at which a driver becomes an older driver.” This fact is further emphasized by Coughlin (2001, p. 2) when he states that the “chronological age is not a perfect indicator of who is an older driver.” According to Marottoli et al. (2000, p. S339), “Caution should be exercised in crafting legislation until acceptable levels of risk are identified in order to avoid over-regulating and unnecessarily preventing large numbers of people from driving, with potential substantial negative effects on their lifestyles.” Rosenbloom (2003, p. 10) has reported that “many countries and a few U.S. states are moving away from age-based testing to behavior-based testing.” Such a strategy has been argued to have merit as

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9 Rosenbloom, in the same article, states, “Age-based testing is rarely useful or costeffective.” Nonetheless, widely reported incidents of tragic consequences resulting from elder driver accidents keep these issues in front of the public and result in different localities or states trying a variety of different strategies to address an acknowledged problem. Driving cessation can be voluntary (i.e., without legal intervention) (Dellinger et al. 2001) or mandated (i.e., forced), stemming from the intervention by a third party such as a family member or court. Driving cessation differs from driving restriction; the latter is a process where individuals manage their impai rment by driving at specific times of the day, along familiar routes and/or avoiding le ft turns for example. In a study by Straight (1997) of drivers and non-drivers over 75 years, it was found that 63 percent of drivers who were active drivers said they avoid traveling at night, 34 percent avoided driving in the rain, and 50 percent avoided driving during rush hour. According to Burkhardt et al. (1998, p. 450) to minimize the negative connotations surrounding the word “cessation” or “quitting,” phrases such as “graduating from driving” or “driving retirement” may be more amenable, especially to males. During a period of driving restriction, trip-making can still be accommodated “without searching for alternative travel modes” (Waldorf 2001, p. 24). This period provides a “window of opportunity” (Wang & Carr 2004, p. 144) for the elderly to consider their future travel needs and transportation modes that may be suitable to meet them. This period of opportunity, evidenced by restrictions in driving behavior, may have a downside if remedial actions are not taken in that “anticipated mobility consequences actively discourage some persons from reduci ng or ceasing driving” (Burkhardt 1999, p. 11). Thus, unsafe drivers continue to driv e, posing a danger to themselves and others while ignoring alternative transportation po ssibilities that may be available. By

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10 considering alternative transportation possibilities during the period of driving restriction, the trauma of being forced to quickly cons ider alternative modes and trip-making behavior when driving has ceased altogether is reduced. 2.3 The Driving Cessation Process It is helpful to understand the concept of driving cessation from an aging perspective. Figure 2.1 presents the process of driving cessation and the ceasing of trips made as a driver and the possibility of future travel being made as a passenger using private or public transportation. It is accepted in the majority of cases that driving cessation is a process and has been appropriately described as a “cessation continuum” by Dellinger et al. (2001, p. 435). Here, the cessation process occurs in stages as a gradual progression of self-imposed restrictions on driv ing, culminating in permanent cessation. Gilley et al. (1991, p. 944) noted, “Cessation of driving is not an all or nothing phenomenon but the eventual end point of a gradual reduction in driving activity.” A similar definition was also expressed by Horowitz et al. (2002). In Figure 2.1, assuming a starting point at age 50, there is relatively little change in the miles driven per year by the individual in these early years of seniority. This period of continued competent driving ability creates a “window of opportunity” (Wang & Carr 2004, p. 144). According to Wang & Carr, during this phase there is the possibility for medical interventions to be applied (e.g., appropriate pharmacotherapy for neurological disease, treatment of reversible ophthalmologic diseases, physical therapy for fragility or muscle weakness and occupational therapy for functional deficits) that may help older adults maintain driving skills and confidence in their driving performance. According to Friedland (1997), factors affecting the duration of this “window of opportunity” are the patient (i.e., the driver), the family, and the medical care

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11 team of the patient. Other factors may include, difficulties of the individual adhering to team advice regarding driving cessation and failure of professionals to inform the individual of impairments impacting their driving ability. 2.4 Seniors Who Retire from Driving Valid estimates of the numbers of senior drivers who give up driving are difficult to derive, as there is no way of determining if the holder of a driver’s license is a regular/intermittent driver or permanent non-driver. As noted by Levy (1995, p. 461), “Not all drivers are legally licensed and not ev eryone who is licensed actually drives.” This scenario is particularly pertinent to the senior population. As recently as 2001, it was noted that “the literature has not yet provided estimates of the current or future incidence of driving cessation” (Waldorf 2001, p. 23). Since that time there have been Miles as Driver Miles as Passenger (Rideshare or Transit) After driving cessation, travel is undertaken as a passenger or by transit Rapid cessation resulting from traffic injury or medical diagnosis Immediate cessation resulting from traffic injury or death 50 85+ Age # Miles Driven/Traveled Window of Opportunity (Wang & Carr) Driving Cessation Continuum (Dellinger et al.) Figure 2.1 The Process of Driving Cessation

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12 several initiatives made to close this knowledge gap. A study of driving life expectancy of seniors in the U.S. by Foley et al. (2002) estimated that 600,000 senior drivers ages 70 years or older stop driving each year. Another more recent estimate indicated that 1 million license holders retire from driving annually (Staplin & Freund 2005). 2.5 Driving Cessation Other Factors Other factors, with the exception of anatomical or cognitive, can be grouped into two categories: gradual/planned and sudden/unplanned. Gradual/planned factors can be classified as being “involved,” i.e., accepting that the impaired person is an adult who has the right to be included in decision affecting his or her life (Jett et al. 2002, p. 111). Such a strategy is time-consuming, and its success is dependent on the level of impairment in the individual concerned. In the case of sudden/unplanned factors, they can be incidentor accident-based or classified as being imposed, i.e., imposed on the individual by other parties, as the individual is unwilling to make the change by himself/herself (Jett et al. 2002, p. 111). 2.5.1 Gradual and Planned Cessation Sixty percent of participants in a study by Campbell et al. (1993) indicated that they voluntarily ceased from driving. Campbell and her colleagues went on to explain that such a response, though commendable, may indicate that these participants had a less severe disease/health condition than those participants in the study who identified a condition that precipitated driving cessation, or the participants may have had a condition but, since its diagnosis, were in a state of denial about its impact. Another factor influencing driving cessation is for the impaired driver to acknowledge the potential danger that they may become to a loved one, neighbor or family pet, if they continue to drive (Jett et al. 2002).

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13 Increasing age increases the chances of driving cessation (Campbell et al. 1993, Stewart et al. 1993). In a study by Dellinger et al. (2001) of those who ceased driving within the previous five years, two percent stopped in their 60s, 18 percent in their 70s, 63 percent in their 80s, and 17 percent in their 90s. Forrest et al. (1997) reported that, as well as driving less with increasing age, women participants also were more likely to use avoidance strategies, such as not using freeways. Horowitz et al. (2002) estimated a five percent reduction in the number of study participants who drove with every year of increased age. Though aging is an accepted predictor in driving cessation, Owsley et al. (1998) pointed out the inappropriateness of guidelines that determine the suitability of driving for older adults based on age alone. Driving cessation is predominately exercised by elderly women (Freund & Szinovacz 2002). A study by Campbell et al. (1993) also found that women were twice as likely to report having stopped driving than were men. A similar finding also was reached in a study by Stewart et al. (1993). Approximately two-thirds of the participants in a study by Dellinger et al. (2001) who had stopped driving within the previous five years were female, though gender differences (w ith respect to driving cessation) did not reach statistical significance. In another study by Foley et al. (2002), women participants were three times more likely to cease from driving when compared to male participants. One reason given by women participants who had ceased from driving, in a study conducted by Dellinger et al. (2001, p. 4), was that “someone else could drive them.” Yassuda et al. (1997) also found that focus group participants preferred other people to make the decision to cease from driving for them. Nevertheless, Dellinger et al. (2001) noted that, for respondents who had ceased from driving, a subjective assessment of the driver’s own driving ability wa s the primary factor in driving cessation, not advice from family or friends. A par ticipant in Bauer & Rottunda’s study (2003)

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14 indicated elderly drivers did not want involv ement of their children in deciding when they should stop driving. Indeed, the majority of participants in Bauer & Rottunda’s study decided for themselves. A similar finding was found in studies by Persson (1993) and Ralston et al. (2001). Campbell et al. (1993) found that participants did not include family as an influencing factor with respect to driving cessation; only the affected individual or legal requirement were involved. Despite the preceding, Hebert et al. (2002) noted that with family members/caregivers there may be difficulty in objectively evaluating driving abilities of the affected loved one; this, in turn, may prolong the period before permanent driving cessation (i.e., lengthe n the driving reduction phase), as they are unlikely to limit or stop their spouse/signi ficant other from driving based solely on diagnosis. The definition of a co-pilot is “somebody available in the car that can directly instruct and supervise” (Jett et al. 2002). In a study by Foley et al. (2000), 10 percent of 59 participants diagnosed with dementia had not ceased from driving at the time of the study. These persons always drove with som eone else present in the vehicle as a copilot, in most cases, the spouse of the driver. Research by Freund & Szinovacz (2002) suggests that the lack of an alternative driver in the home kept cognitively-impaired women on the road, especially where a spouse who may have been the primary driver had been outlived. Co-piloting as a strategy may work for a limited time, but, in situations where a decision is required quickly, driver response may be insufficient. Thus, it becomes a strategy that is not recommended in the process of driving cessation (Hartford Financial Services Group, 2000). Some medications can affect driving skills, which, in turn, will influence driving cessation. Medications that may impair driv ing skills include antidepressants, hypnotics, antihistamines, glaucoma agents, and muscle relaxants (Carr, 2000). However, in a

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15 study by Stewart et al. (1993), it was found that specific drug ingredients or the total number of drugs used were not a significant ri sk factor for driving cessation, a surprising result to the study team. Yassuda et al. (1997) noted that the low frequency of the medication topic (i.e., a participant response from the survey instrument) may reflect the participant’s lack of knowledge of the effects of drugs on driving ability, denial that drugs had any negative effect on driving, or even the belief that taking medicine was a part of normal aging. 2.5.2 Sudden and Unplanned Cessation A life event may precipitate driving cessation (see Figure 2.1). Such an event may be in the form of a diagnosis of a disease or a personal loss such as the loss of a spouse/partner. In a study by Bauer & Ro ttunda (2003), such life events experienced by participants ranged from a heart attack to a fall. A traffic crash often is a precipitator of driving cessation, especially when the i ndividual had been advised against driving while managing some form of impairment. According to Dobbs et al. (2002), while life events may have a severe negative impact on driving ability (in the case of the diagnosis of dementia), this should not be the sole justification for the revocation of a driver’s license, which, in turn, can bring about an immediate cessation of driving. As already noted, gradual change in the process of driving cessation will allow managed interventions, where various parties may become involved in the decision for an individual to cease from driving. On the other hand, if such interventions by persons closest to the affected individual are not forthcoming, medical professionals and/or government agencies, i.e., state driver’s license agencies, have a “moral and legal obligation to care for the demented individual and to protect the safety of the public” (Berger & Rosner 2000, p. 306). Campbell et al. (1993) found that the potential of

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16 license revocation/cancellation if driving is not curtailed, significantly increases the odds of driving cessation among elderly persons. The potential loss of insurance coverage (Carr 2000) also may bring about a sudden loss of driving privileges for the affected individual, leading to rapid or immediate cessation of driving. The revocation of the driver’s license by a third party has the potential to have the opposite effect. Burkhardt et al. (1998) identified research that concluded older drivers might be more likely to resist driving cessation, while claiming that a third party (e.g., state driver licensing authority) had forced them to continue driving by taking away their license prematurely. 2.6 Demographics Results from the U.S. Decennial Census in 2000 showed that persons 65 years and over represented 12.4 percent (35 million persons) of the total population. Population forecasts for the year 2030 indicate that this same age cohort will be more than 20 percent of the entire U.S. population. The primary reason resulting in this scenario is the maturing of the baby boom population, i.e., those persons born between 1949 and 1965. In 2000, the baby boomers would have been between 35 to 51 years, and as evident in Figure 2.2, this cohort causes the bulge in the population pyramid. Over the next few decades as this bulge matures, i.e., moves upwards, it will cause a “squaring” of the population pyramid away from the typical pyramid shape seen in nations with large youthful populations coupled with small elder populations. This squaring of the population pyramid is clearly depicted in Figure 2.3, reflecting population estimates for the year 2030.

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17 Figure 2.2 United States Population 2000 Source: Generated in September 2006 using data from the U.S. Census Bureau International Data Base Baby Boom Generation Figure 2.3 Projected United States Population 2030 Source: Generated in September 2006 using data from the U.S. Census Bureau International Data Base Baby Boom Generation

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18 Figure 2.4 indicates that the forecasted percentage of persons of the total U.S. population over 65 years of age will continue to grow for the foreseeable future. By the year 2030, the number of persons 65 years or older (estimated to be over 71 million) will have increased by more than 100 percent (based on Census 2000 population figures of 35 millions for persons ages 65 years and older). 2.7 Closing Gap of Licensure Rates by Gender In the year 2000, 92 percent of males over t he age of 65 years in the U.S. were licensed, compared to 68 percent of females, a difference of 24 percentage points (Office of Highway Policy Information 2001; U.S. Census Bureau 2004). As illustrated in Figure 2.5, in the year 2000, with each age cohort less than 65 years, the percentage of licensed persons increased while the difference between the proportions of persons Figure 2.4 Percentage of Persons 65 Years or More of Total Population (2000 – 2030) Source: U.S. Census Bureau ( 2004 ) 0% 5% 10% 15% 20% 25% 2000200520102015202020252030% Total Population 65+ Years0% 20% 40% 60% 80% 100% 120%% Growth Of 65+ Cohort From 2000 Percent of Population 65+ Years Growth of 65+ Years Cohort From 2000

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19 licensed according to gender decreased. For example, for the age cohort 55 – 64 years, 97 percent of males and 89 percent of females were licensed in 2000 compared to the 35 – 44 year cohort, where 95 percent of males and 93 percent of females were licensed at that time. It will become evident that, with each passing decade, the differences between male and female licensure rates will close and stabilize above 90 percent. 2.8 Moves Toward Age-Based Driver Testing by State Licensing Authorities In Chapter 1, it was noted that several U.S. states in recent years have implemented “age-based” driver licensing regimes. In 2001, 33 states did not require any further licensing requirements as people aged (Coley 2001). In contrast, 18 states in 2001 did require seniors to fulfill age-based require ments when applying for or renewing their Figure 2.5 Percentage of Population Licensed by Age Cohort and Gender in 2000 Source: U.S. Census Bureau 2005 & OHPI/FHWA 2001 30% 40% 50% 60% 70% 80% 90% 100% 35 44 Years 45 54 Years 55 64 Years 65 74 Years 75 84 Years 85+ Years Age CohortPercent Licensed to Drive (Yr 2000) Percent Male Licensed Drivers of Male Population Percent Female Licensed Drivers of Female Population

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20 drivers’ licenses. In the space of four y ears, as of July 2005, the number of states imposing age-based requirements on seniors when applying for or renewing their drivers’ licenses increased to 24, a 33 percent increase (AAA 2005). The threshold for accelerated renewal (i.e., a situation where the frequency of testing is increased once an age threshold is reached) also varies from state to state. According to Molnar & Eby (2005), “the beginning age for accelerated renewal ranges from 61-years-old (Colorado) to 81-years-old (Illinois).” Indeed, under this regimen, the time validity of licenses also is impacted, ranging from “1 year (Illinois for age 87 and older) to 5 years (Arizona, Colorado, and South Carolina)” (Molnar & Eby 2005). Age-based testing is one of several strategies used to assess the driving ability of seniors as they age. According to research by Cobb & Coughlin (1997), there are three principal tools used to identify unsafe senior drivers: assessment or judgment of the driving examiner – the single most important control in all jurisdictions; screening of the person’s driving record; and medical reporting. The “in-person” assessment of a senior driver by a driving examiner under the “age-based” testing regimen has resulted in a variety of benefits and disadvantages arising as a result of this strategy. Benefits of accelerated licensing periods (i.e., seniors renewing at shorter periods than adults younger than them) according to Levy (1995) can be described as: increasing the visibility of a policy to the target population; reducing the length of period before a problem is detected; increasing the likelihood of recognizing problems of individual applicants; and learning about the changes occurring to individuals over the years (Coley 2001).

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21 On the other hand, disadvantages or disbenefits of an accelerated licensing regime according to Lange & McKnight (1996) can be described as: discouraging driving and associated activities by seniors; license revocation of drivers who fail the tests; self-regulatory termination of driving by those who fear that they cannot pass the test (these may be insecure but safe drivers); and withholding of pertinent health information with respect to personal driving ability drivers may be afraid to mention certain sy mptoms, if they fear that acknowledging a specific ailment will jeopardize their right to drive (Walser 1991). The gradual implementation by states of age-bas ed testing of senior license holders is based on the premise that senior drivers pos e a greater risk to themselves and other road users the older they become. Despite the supposed benefits of such a scenario, there have been a number of concerns expressed against unwarranted moves in this direction. First, “laws imposing requirements only upon those above a certain age may be discriminatory if they do not produce clear safety benefits; and second; in the absence of specific medical problems, age alone has not been shown to be associated with poorer driving performance” (TRB, quoted in Rock, 1998, p. 69). In light of these two statements, the implementation of agebased testing may precipitate driving cessation by those applicants who fail (on their initial attempt) or current drivers (with accident free driving histories) who choose not to renew their licenses for fear of failing. Safe drivers who prematurely have to end their driving will suffer inordinately due to the frustration of adjusting their travel behavior to accommodate an unanticipated new mobility regime. This frustration is likely to be exacerbated if alternative transportation is limited or not available. Here arises a dichotomy, as; on the one hand, society may

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22 precipitate driving cessation of senior drivers, while on the other the provision of alternative transportation modes are inadequate or nonexistent. Stamatiadis et al. (2003 p. 49) noted this concern where it was stated that “age-based license restrictions pose numerous society questions regarding the availability of travel alternatives for persons without drivers’ licenses.” Several studies have found positive correlation between age-based licensure laws and safety with respect to senior drivers (Nelson et al. 1992; Levy 1995). Such a scenario may be achieved through reducing licensure rates of seniors, which possibly may contribute to an enhanced traffic safety environment. On the other hand, Rock (1998), using Illinois crash data, found a tenuous relationship between frequency of license renewal (for persons 81 years and above) and crash rate, thus producing negligible benefits of such a policy. Results from a study by Lange & McKnight (1996) also called into question “the ability of age-based renewal testing to yield significant reduction in proportions of unsafe drivers among the elderly.” In Walser (1991, p. 4), …. an experiment conducted in Pennsylvania suggests that states need to monitor older drivers even more aggressively. It also indicates the tremendous social ramifications that would result if even present-day tests were used across-the-board. Between 1978 and 1985, licensing officials used a computer to randomly select 365,000 drivers over age 45 (the majority were over 65) and notified them that their licenses would not be renewed unless they came in for general physical and eye examinations. Of those who were examined, more than 77,000 subsequently had new restrictions added to their licenses. Almost as

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23 many -some 72,000 -chose not to come in for the exams, and their licenses expired as a result. It is accepted that there is a link betw een chronological age and driving performance; however, “the probability of deteriorating performance increasing with increasing age, the presence of individual variation means that no specific chronological age can be singled out as an appropriate age at which a driver’s license should automatically be denied” (Waller 1991, p. 502). Indeed, trying to weed out unsafe drivers according to age is a challenge in an environment where seniors are living longer, healthier, and more active lives. McKnight (2003) notes that few age-related declines in ability are susceptible to experimental variation. This is despite having the same “cause” (i.e., disease) and “effect” (i.e., the impact of the disease on driving ability) either of which may not be explicitly controlled for in some cases. This scenario is also confirmed by Messinger-Rapport & Rader (2000), who indicate that there is no single predictor of adverse driving events (which may be a precursor to driving cessation) that can be applied in the office (i.e., under experimentation). 2.9 Seniors Potential to be More Dependent on Outside Resources for Mobility Contemporary socio-economic, demographic and cultural trends could lead one to anticipate that future seniors are more likely to live alone, less likely to have as many children in proximity, less likely to have sib lings in proximity, and less likely to live in locations with quality transit and walkable destinations than prior generations. These conditions are the result of the number of trends that have been underway over the past several decades. This includes lower fertility rates, i.e., fewer siblings and children, high rates of divorce, and high immobility levels resulting in more frequent relocations away

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24 from family and friends. Figures 2.6 and 2. 7 present indicators from the Decennial Census and Survey of Income and Program Participation (SIPP) that provide insight into the “personal environment” of seniors. Figure 2.6 indicates that between the years 1990 and 2000 (in the age cohorts illustrated), there were increases in the percentage of seniors whose living arrangements were described as “living alone.” The increase in “living alone” status for all persons 65 and older approximated 2 percent; there was a 3 percent increase for persons 75 years and older. One might expect this trend to in crease dramatically as the numbers of baby boomers, a generation accustomed to high mobility and divorce and noticeably fewer and more mobile offspring, increases. This may be particularly true for females, who tend to outlive males. In very practical terms, this means there may well be poor households where an individual who has ceased driving does not have other household members available to provide mobility. Figure 2.6 Living Arrangements of Adults 65 Years and Older 1990 and 2000 Sources: Fields & Casper (2001) and Goldstein & Damon (1993) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 65 to 74 Yrs75+ YrsTotal 65+ YrsPercent Living Alone Year 1990 Year 2000

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25 Exploring the need for personal assistance as one ages, Figure 2.7 illustrates the percent of seniors who needed assistance with activities in 1986 and 1991. It is evident from Figure 2.7 that the need for personal assi stance with everyday activities (e.g., care, preparing meals, etc.,) increases with age. Hobbs & Damon (1996), reporting on data from the 1991 SIPP, noted that women 75 years and older were more likely to require more assistance than men, and elderly Hispanics or African Americans may require more personal assistance than Whites. This also includes assistance needed to get around outside of the home. This factor may be indicative that driving oneself may no longer be an option for the senior and they are thus dependent on others for transportation; the use of public transportation may be a challenge even if it is available for seniors in this predicament. Figure 2.7 Persons Who Needed Assistance with Activities by Age Source: Hobbs & Damon (1996) p. 3-18 & Harpine et al. (1990) p. 21 0% 10% 20% 30% 40% 50% 60% 65 69 Yrs70 74 Yrs75 79 Yrs80 84 Yrs85 Yrs+ Age CohortPercent Needing Assistance 1986 1991

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26 The combination of socio-demographic changes, societal and family structure changes is resulting in a situation where the next generation of seniors is less likely to have their mobility needs met in the same ways as prior generations. Indeed, future seniors are less likely to have familiarity with transit use and may be less likely to consider it. This potential scenario is confirmed by Kostyniuk & Shope (2003), where, in their study of 1,000 senior drivers (active and former) in Michigan, 60 percent of the respondents indicated that they had never used public transportation in their lives and, of those who had, the experience was acquired a long time ago. Simultaneously, auto travel on the ever more congested roads is like ly to be higher risk for senior drivers. The collective impact of these changes is likely to complicate the already difficult challenges of meeting mobility needs for post-driving cessation seniors. 2.10 Household Composition and Driving Cessation Persons living with a senior driver do have a ro le to play in the driving cessation process, despite the fact that some drivers who ceas e to drive have indicated that they made the decision themselves without outside influence. Household composition is, therefore, a factor in the driving cessation process. Kington et al. (1994, p. 1329), in a study of 2,429 respondents, found that “individuals who lived in households with more adults were less likely to drive.” The research team went on further to note that this situation may have arisen because, where there are other adults in the household who are able to drive, those who can no longer drive choose to live or remain in such households. In a study of Public Use Microdata Sample (PUMS) from the 1980 Census of Population, Cutler & Coward (1992) were able to determine that the majority of elderly persons (77%) live in households where personal transportation was available. Nevertheless, Cutler &

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27 Coward were not able to determine how many of these persons actually were drivers or passengers (as the census data collected did not permit this). In a study by Taylor & Tripodes (2001, p. 521) it was found that “the composition of the household in which an elder lives also determines the transportation resources available … as the presence of a licensed driver in the home was the most important predictor of perceived mobility following driving loss.” Waldorf’s study (2001 p. 33) came to a similar conclusion where it was found that the “presence of an additional driver in the household is the single most important factor influencing whether older people intend to use alternative transportation modes.” 2.11 Trends Influencing Senior Transit Use or Non-use In arriving at an estimate of senior drivers and non-drivers (i.e., never driven and former drivers) that could form a potential market for transit agencies in future years, it is also necessary to identify trends that currently influence transit use or non-use by seniors. These trends may continue to develop in future years, in turn increasing or decreasing the potential of seniors to consider transit use as a viable transportation alternative to the Personally Operated Vehicle (POV). Burkhardt et al. (1998, pp. 4) during their study identified several notable trends affecting older people and their transportation choices. These trends can be listed as follows: Spatial Dispersion characteristics Aging in place Unequal income distribution The predominance of women Minority elders Urban/rural differences Changes in family structure Health status Retirement status

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28 2.12 Trends Having Positive Potential on Senior Transit Use Trends that may have a positive influence on transit use (i.e., increase the propensity to use transit) by seniors are presented. 2.12.1 Minority Elders Acknowledging the impacts of race on driving cessation, Rosenbloom (2001) found, in a study of 1,000 volunteer drivers (current and former) in Tucson, AZ, that Hispanics (of any race) who made up 6.5 percent of the sample population comprised 12 percent of ceased drivers at follow up whereas African Americans were, on average, the youngest (69 years) to cease from driving of all raci al groups surveyed. Future decades will bring gains to African American/Black and Hispanic ethnic groups with respect to their percentage makeup of the senior U.S. population. However, it is estimated that there will be a decline from 84% to 72% percent in the numbers of seniors classified as ‘White Alone’ of the total population between 2000 and 2030 (U.S. Census Bureau 2004). These estimated changes in the proportions of seniors according to their ethnic heritage may have a positive impact on future levels of transit use, as research has shown that minority populations have had a greater propensity to use transit than the majority nonHispanic White population (Polzin & Chu 2005). 2.12.2 Number of Adult Children The family characteristics of senior households are and will remain paramount in deriving an estimate of the number of seniors that may avail themselves to transit. As already stated, “the number one alternative to the car for older adults is not another mode: rather, it is riding with family members and friends” (Coughlin 2001, p. 3). If members belonging to this group are not avail able within the immediate locale of the

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29 non-driving senior, opportunities may arise to consider transit as a out-of-home mobility option. The Decennial Census does not provide information that enables detailed familial linkages (on a macro scale) to be determined, i.e., mother living with daughter, etc. Nevertheless, proxies in the form of fertility rates and the parent support ratio may provide insight. What we do know with respect to these proxies can be summarized as follows: Since the Baby Boom period (1946 to 1964), the general fertility rate (i.e., live births per 1,000 women ages 15 to 44 years) has fallen from a high of 118 in 1960 to 66 in 2000 (CDC, 2000), graphically displayed in Figure 2.8. The parent support ratio (i.e., “the number of people 85 and older per 100 people aged 50 to 64 years” [He et al. 2005, p. 26]) has been increasing over the recent decades, from 3.4 in 1960 to 10.1 in 2000 and is predicted to increase to 16.0 in 2030 (graphically displayed in Figure 2.8). What this rate implies, taking the year 2000 as an example, is that for every 10 persons ages 50 to 64 years there could be one oldest-old family member to attend to. The parent support ratio is a socio-demographic concept and does not indicate that every family or individual will have an oldestold family member to care for or, on the other hand, that an oldest-old member of the community will not have someone to assist with personal transportation (i.e., a driver) available. As the absolute number of persons ages 85 years or more increases relative to the shrinking numbers of adult children (i.e., persons ages 50 to 64 years who may provide assistance to the elder), so too does the parent support ratio increase. What this scenario suggests is that there may possibly

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30 be a greater need for alternatives to POV transportation for the oldest-old in 2030 as the pool of drivers available (i.e., adult child of senior) will be reduced. 2.12.3 Physical Distance of Adult Children Falling fertility rates have resulted in fewer children being born to women who live longer, increasing the probability that in future decades these fewer children (then adults) will have an older parent to look after. However, the effectiveness and sharing of parental care and responsibility by living adult children will in some cases be dependent on the spatial separation between the adult child/ren and their parent/s. What is known about parental/child relationships separated by physical distance, can be summarized as follows: Figure 2.8 Fertility Rates and Parent Support Ratios 1960 to 2030 Source: CDC Table 1-1. Live Births, Birth Rates, and Fertility Rates, by Race: United States, 1909-94 & U.S. Census Bureau (2004) Projected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) U.S. Census Bureau, Popul ation Division, Populat ion Projections Branch 0 20 40 60 80 100 120 140 19601970198019902000201020202030Live Births per 1,000 Women 15-44yrs0 2 4 6 8 10 12 14 16 18# People 85yrs+ per 100 People 50-64yrs Fertility Rate Parent Support Ratio

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31 “Distance is the strong predictor of assistance exchanges among family members that require a physical presence” (Rogerson et al. 1997, p. 122). “Daughters are more likely than sons to provide informal support to elderly parents and it is not family size but the presence [or spatial proximity] of a daughter that affects the level of parental help” (Spitze and Logan quoted in Rogerson et al. 1997, p. 124). “Having more children increases the likelihood that there will be at least one suitable child toward whom parents may expect to move closer” (Silverstein & Angelelli 1998, p. S158). The higher the educational level [of either party] the greater the spatial separation between them (Lin & Rogerson 1995). “Children with remarried parents are less likely to living within an hour of their parents” (Lawton et al. quoted in Lin & Rogerson 1995). Lin & Rogerson’s study (1995) using data from the National Survey of Families and Households (NSFH) (conducted during the mid 1980’s) reported that 75 percent of elderly parents had an adult child living within 35 miles (Lin & Rogerson 1995, pp. 317). However, since the 1980’s there has been a dramatic structural change in the spatial distance between family members brought about by employment opportunities, increases in personal educational levels, mass transportation linkages, etc. In light of this scenario, in future decades with women having fewer children, there will be fewer children living nearby for whom aging parents can move towards, resulting in possibly an even greater propensity to age-in-place for those parents who choose not to move or relocate to assisted living/nursing home facilities. For non-driving community-dwelling

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32 seniors in this situation, transit may have the potential to be considered as a viable transportation option. 2.12.4 Marital Status of Elderly Population It is accepted that marital status can affect many facets of an individuals life, including longevity, health, income etc. (Lillard and Panis, quoted in He et al. 2005). Thus, as marital status may change during an individuals life, so too will their proclivity for travel. For example, an active and healthy senior, is likely to make more daily trips, perhaps to see friends and the family of each partner, than in the case of an active and healthy single senior. Such a scenario is evident from an analysis of NHTS trip data and presented in Table 2.1. Table 2.1 indicates that, in the case of the 2 person household (where at least one member is a senior), a daily trip rate of 3.68 can be compared to a one person senior household of 3.31. Table 2.1 Daily Trip Frequency According to Household Size Trip Category All Households One Person Household Two Person Households All Person Trips (billions) 407.333.1 108.4 Senior (65yrs +) Trip s (billions) 41.010.2* 25.5* Daily Trip Rate All Persons 4.034.03 4.07 Daily Trip Rate Seniors 3.423.31 3.68 *Note at least one person in Household is 65 years or more Source: Author’s analysis of NHTS (trip and person files) data Research has indicated that, once the age of 65 years and above is reached, “divorce is relatively infrequent among the older population” (He et al. 2005). No one doubts the impact of divorce on the affected parties and families but with respect to seniors meeting their daily travel needs, there will also be an acute impact. That is, divorce may have a negative “impact on the amount of time and money that is exchanged later in life between adult children and their fathers, with less impact on their mothers” (Furstenberg

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33 et al., quoted in He et al. 2005, p. 148). Thus, there is the potential that the “time” aspect may include the time available for transporting senior parent/s by their adult children. This scenario increases in complexity when one considers that the “victim”’ of the divorce may be supported by the adult child/ren while the other parent is not. For non-victim seniors (of a divorce) in this situation, transit may have the potential to be considered as a viable transportation option. 2.12.5 Technology and Design In recent decades, there has been increasing application of computers and technology to transit service and operations. Working alongside these applications Intelligent Transportation System technologies (ITS) also has played a role in enhancing transit service quality. Examples of technological innovations that may positively enhance transit use by seniors in the future as follows: Low floor vehicles Step-less entry into the vehicle, i.e., there is no need for the passenger to step-up or step-down to access/exit the vehicle. Vehicles are also accessible to persons in wheelchairs and passengers pushing baby strollers or grocery karts. Benefits of such an intervention include, ease and speed of access/exit from the vehicle for all passengers but notably those who may require extra assistance, e.g., seniors. Global Positioning Systems (GPS) A wireless navigation system that enables v ehicles to be tracked and located. Thus, real time information as to vehicle location, speed and estimated time of arrival can be obtained and disseminated. A benefit arising from this technology in the form of accurate travel information for exampl e, enables improved planning and execution of the trip by the passenger.

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34 Internet/Cell Phones Recent decades have seen the rapid application of internet and cell phones in the dissemination of transit service/trip information. Benefits from this form of technology can be experienced in the potential of “real time” transit information being obtained before, after and while on the trip. This enables a potential or an actual passenger to plan in advance a transit trip to meet their exact needs. 2.13 Factors Having Negative Potential on Senior Transit Use Trends which can have a negative influence on transit use (i.e., decrease the propensity to use transit) by seniors are presented in the following paragraphs. 2.13.1 Life Expectancy and Health Over recent decades, much progress has been made in the field of science, most notably medicine with its positive impacts on morbidity, disease progression and management and ultimately life expectancy. In fact, life expectancy at 65 years over the last 4 decades, (i.e., from 1960) has increased by 0.9 years per decade for males and females. Thus, persons ages 65 years in 2000 should experience a life expectancy of 81.2 years (males) and 84.3 years (females) (Nat ional Center for Health Statistics 2005, Table 27. p. 167). However, it is interesting to note that despite the equal gains in life expectancy for males and females ages 65 years, in recent decades greater inroads have been made in male life expectancy. A more circumspect measure relating to personal health status which is of relevance to driving cessation, is the amount of time spent free of disability, referred to as “Active Life Expectancy.” This is a period where activities of daily living (ADL) (i.e., personal maintenance tasks such as eating, getting in and out of bed, etc.,) can be performed without assistance (i.e., from a caregiver or an external prosthetic). Research

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35 has shown that with the increasing onset of limitations in performing ADL this signals a change in driving capability for those seniors who do drive (Foley et al, 2002). Table 2.2 presents data on life-, activeand driving life expectancies. Table 2.2 Life-, Activeand Driving-Life Expectancies by Age Life Expectancy* Active Life Expectancy** Driving Life Expectancy*** Age MalesFemalesMalesFemalesMale Females 65 16.219.313.715.7na na 75 10.112.37.78.38.0 7.9 85 5.66.74.23.12.0 1.8 as at 2000, National Center for Health Statistics, 2005, p. 167 ** data from National Long Term Care Survey 1982 to 1994 in Manton & Land, 2000 *** data from Health Dynamics Among the Oldes t Old Study 1993 to 1995 in Foley et al, 2002 It is evident from Table 2.2, that for both males and females there will be a period of life where driving themselves will not be possible. At age 65 years, for many seniors, health status does not interfere with their driving capability. Nevertheless, at the onset of the ninth decade of life, things begin to change. As can be seen at 75 years, men have a life expectancy of 10 further years, of which, eight will be spent in good health free from limitations in performing ADL. The active life and life expectancy data as presented in Table 2.2 is similar to the surviving and surviving and driving curves developed by Waldorf & Pitfield (to be discussed in the following chapter). Driving capability is dependent on adequate vision, physical function and cognitive function all present at acceptable levels during the active life expectancy stage of life. Declines in any one of these factor s coupled with limitations in the performance of ADL can often render seniors incapable of using transit. Indeed, in extreme cases, an escort (i.e., caregiver) may be necessary, having the potential to increase the challenge of using transit, as two persons are now involved instead of one. Improvements in medicine and health, may extend the active life expectancy period, enabling those who drive to continue driving for a few more years. This extension in driving history

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36 benefiting mobile seniors may result in a decreased potential for them to consider transit as they near the end of their driving car eers and when driving is ceased. These seniors may be too ill to consider transit as an option for out-of-home mobility. Indeed, if seniors are to move for health or other reasons, “parents are more likely to choose daughters than sons” (Silverstein & Angelelli 1998, p. S158), Such seniors seeking to maintain pre-cessation mobility levels or to reside in an environment where others can assist with transportation needs, transfer to become car passengers (their initial preference) rather than transit patrons. 2.13.2 Aging in the Suburbs Research has shown that the majority of older people do not move (He & Schachter 2003, pp. 2) and this fact has contributed to the phenomenon “aging in place.” Frey (2003) determined that, of the age cohort 35 to 64 years, approximately 70 percent of residents of large metropolitan areas lived in the suburbs. Accepting that this same cohort has a greater propensity to move (i.e., employment relocation/opportunity, changing real estate needs, etc.,), it is predicted that the majority of moves in future decades by adults ages 35 years and older will be either intra-suburb or from suburbs to outside the metropolitan areas. In the typical suburb, with its less dense transit services, if such service densities remain unchanged in future years (or do not change to offer a real transportation alternative to seniors), it is unlikely to provide what seniors will consider transit as a viable alternative. Gentrification of urban cores (precipitating suburban/rural to central city migration), often associated with transit rich environments, may continue during the intervening years. Nevertheless, it is predicted that young professionals, childless couples, and a small percentage of affluent seniors will be those who take up this trend.

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37 For many seniors, the suburban or rural environment has been perceived as being more attractive for raising a family or establishing family roots. Once children are grown and leave their parents’ home, for many seniors, remaining in the same house/home is usually their preferred choice. On the other hand, focus group participants who had reduced their driving indicated that they rarely traveled to/from and avoided travel through downtowns. Remembering how downt owns were in years gone by and their current state (i.e., parking, one-way streets, and traffic congestion) increased the aversion of some focus group participants of going near downtowns, let alone relocating to reside there. Further insight gained from the focus group discussions, particularly relevant for the Sunbelt states, was that for some participants they would rather remain in a warm environment with limited out-of-home mobility (due to lack of transportation) than to relocate back to the Northeast/Midwest with its transit rich cities but also cold winters. The possible reasoning for this was that having limited transportation options in a Floridian winter but still being able to get out was better than having extensive transit services in the neighborhood but not being able to get out because of the cold and snow. 2.13.3 Technology In recent decades, there has been increasing application of computers and technology to the operation of the POV. Working in tandem with these applications ITS has played a significant role in enhancing POV operati ons and efficiency. Examples of POV technological innovations which may negativel y impact transit use in the future by seniors are as follows:

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38 Congestion Management Systems Systems that mitigate and manage traffic c ongestion, many of these systems are being deployed by many local authorities in the U.S. These systems (i.e., those that harness ITS technologies) aim to optimize vehicle mobility in congested traffic environments. A benefit arising from the im plementation of these systems is seen in the optimization of vehicle delays enabling travel time savings to be realized. In-Vehicle Technologies Currently, there are available a variety of in-vehicle technologies available whose primary benefits are seen in enhancements in driving safety and comfort. Adaptive Cruise Control systems (ACC) maintain a preset driving speed simultaneously keeping a safe distance between a vehicle and the vehicle in front. Rear view video cameras mounted on the back bumper (coupled with sensors) enable the area immediately behind the car to be seen and an alarm to sound if an object gets too close to the car. This latter device is particularly useful in its potential reduction of reversing accidents by seniors who may have difficulty turning their head to gain a correct view of the area behind the vehicle. Increasing incidence of minor bumps and scratches on a vehicle are often tell-tale signs that the senior driver may be losing driving competency. 2.14 Transit Service Provision Planning Understanding the travel behavior of seniors post-cessation of driving may contribute to informed planning and policy making to support the mobility of non-drivers in their communities. Transit providers should note that “the [transportation] needs of older citizens are predictable so accommodating them is possible” (Freund 2004, p. 114). In the coming decades, more and more seniors will have had the experience of driving and

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39 the longer an individual drives, the more accustomed they become towards driving and, as noted earlier, the less likely they are to cease from driving even after diagnosis (of a condition that affects driving ability) and the greater risk they become to other road users (Adler et al. 1999). Transit providers have to realize that, to increase the probability of seniors who having ceased from driving (especially those in the ninth decade of their lives) and who may consider and subsequently use transit, promotion of transit services as a viable transportation alternative must be affected during the driving reduction stage. Such a need is confirmed by Waldorf (2003, p. 198) w ho states that, “the provision of transit services needs to be complemented with programs ensuring that the elderly will actually use transit alternatives rather than choose immobility. It seems necessary that such programs reach the elderly early, well before they are forced to stop driving, so that there is sufficient time to learn about these alternatives, appreciate them as alternatives that can ensure an active life … and learn how to use these alternatives.” In light of this potential scenario, transit providers are cautione d that the promotion of transit services to seniors after driving cessation is likely to yield limited results. Indeed, maintaining the interest of seniors in transit cannot be taken for granted by transit providers. Public transit must assess the markets where its current strengths lie, consider what new markets exist or are evolving, evaluate how these new markets can best be served, and evaluate the areas where it is possible to strengthen the role of public transit. … [Transit] operators who do nothing to deal with the major changes in the travel patterns of most Americans … will see their ridership erode and their public political support with it (Rosenbloom & Fielding 1998, p. 1).

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40 Policy change often takes many years, and transit agency reaction to it may follow. Informed planning decisions (based on research such as herein described) may enable transit providers to become increasingly adept (i.e., proactive) in meeting the mobility needs of seniors in the years ahead.

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41 CHAPTER 3 – DATA AND METHODS 3.1 Introduction As already noted in section 2.4, valid esti mates of the numbers of senior drivers who may give up driving (i.e., creating a potential transit market) are a challenge to derive, as there is no direct method to determine whether the “bona fide” holder of a driver’s license is a regular versus an intermittent driver or permanent non-driver. Even if a senior driver license holder is asked, “Do you drive?” some respondents may answer in the affirmative (i.e., pretend to drive when in fact they do not drive) in order to appear functional in their old age (Burkhardt et al, 1998, p. 24). Furthermore, as noted by Levy (1995, p. 461), “Not all drivers are lega lly licensed and not everyone who is licensed actually drives.” This scenario is particularly pertinent to the senior population. In 2001, it was noted that “the literature has not yet provided estimates of the current or future incidence of driving cessation” (Waldorf 2001, p. 23). However, a study of driving life expectancy data of seniors in the U.S. (1993 to 1995) by Foley and colleagues estimated that 600,000 persons 70 or older had stopped driving during the year of study (Foley et al. 2002). Recent ly, an estimate of 1 million license holders who retire from driving annually was made in 2005 (Staplin & Freund 2005). Despite these estimates, a contribution to permit an increased understanding of the current methodologies to estimate the future numbers of senior drivers who will be reducing their driving exposure or will have ceased altoget her is still warranted. The challenge in deriving an appropriate methodology is also associated with understanding the future licensing rates of senior women coupled with their levels of driving exposure. (Burkhardt

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42 et al. 1998) This synthesis of methodologies described herein aims to further close the gap in the development of a definitive methodology that is able to determine the numbers of seniors who reduce or cease from driving. 3.2 Methodological Approach If the current status-quo of senior mobility is perpetuated into the future, transit operators will be faced with an “adapt or perish” quandary. To contribute to the refinement and understanding of a potential future senior transit market for transit providers, the methodology described will seek to determine the following: estimate the number of seniors at a specified future year; estimate the number of seniors holding driver ’s licenses at a specified future year; estimate the proportion of seniors who may have ceased from driving (i.e., who are driving intermittently or have permanently ceased to drive); and estimate the transit market share based on the use of transit by seniors who are either non-licensed or former drivers. Such an estimate may indicate a potential market for public transit as one of several transportation alternatives. With an enhanced composite profile of the senior traveler, this research also aims to enable a clearer understanding of the market characteristics that transit agencies may be able to target in future decades and determine the nature of travel needs that senior travelers have, permitting transit providers to target their services accordingly. The research methodology proposed will try to determine if future generations of senior nonlicensed and former-drivers may consider tr ansit use as a viable mobility option (of several transportation alternatives) and identify the extent to which transit operators can

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43 positively contribute to senior well-being through the provision, access to and use of their services. 3.3 Quantitative Methodology and Primary Data Sources Investigation will be achieved primarily through quantitative (i.e., interrogation of datasets) analysis, followed by the presentation and discussion of results in Chapter 4. Quantitative research methods will involve the analysis of public datasets of travel behavior, senior population characteristics, and so on. An overview of a selection of publicly available datasets that enabled t he creation of a senior profile through quantitative analysis is presented in this section. 3.3.1 National Household Travel Survey The National Household Travel Survey (NHTS) is a dataset of long-distance and local travel behaviors by the American public. Collection of data for the NHTS is sponsored by Federal Highway Administration (FHWA), Bureau of Transportation Statistics (BTS) and the National Highway Traffic Safety Admi nistration (NHTSA). The NHTS is recognized as the leading dataset in the U.S. providing detailed information on trip-making behavior. The most recent year for this cross sectional study was 2001; the survey has been conducted intermittently since 1969. Data items on individual trip making include mode of transportation used, duration of trip, distance and purpose of trip. In addition to person trip characteristics the NHTS also provides trip maker information relating to demographic, geographic, and socio-economic profiles. 3.3.2 Decennial Census The Decennial Census is the authoritative census of the U.S. population conducted every 10 years. The latest census occurred in the year 2000; the status quo of the

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44 current population and estimates of future populations are conducted on an ongoing basis. The Decennial Census provides a rich data resource on many macro-aspects of the general population, e.g. race, household characteristics, etc. 3.4 Quantitative Data Sources (Secondary) An overview is given of other datasets that also contributed in the development of the senior profile, in particular, estimates of former drivers. 3.4.1 Health and Retirement Study The Health and Retirement Study (HRS) is a longitudinal survey of a nationally representative sample of the senior population (51 years and older) in the U.S., which since 1996 has been conducted every two years. The main goal of the HRS is to “provide panel data that enable research and analysis in support of policies on retirement, health insurance, saving and economic well-being” (Rand Center for the Study of Aging 2006, p. 10). The study is funded by the National Institute on Aging (NIA) and provides data on senior health, income, assets, employment, retirement, insurance and family structure. The first HRS study was conducted in 1992, and the latest year in which data is publicly available is 2002. 3.5 National Versus State Level Analysis The analysis presented here is at the national level. This is due to the nature of data required to derive future estimates and the focus of this study being at the macro rather than micro level. For the majority of factors described below, as at the time of writing, future estimates were only provided at a national level.

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45 3.6 Base Year To undertake a prediction of some future event establishing a base year is imperative. In this study, the base year is 2000/2001. There are several reasons for setting the year 2000/2001 as the base year: The year 2000 is the most recent year of the U.S. Decennial Census. This year also provided a platform for revised population projections of the U.S. Census Bureau, which have since been published in recent years. Within two years on either side of 2000/2001 (i.e., 1998 to 2002), a number of cross sectional and longitudinal surveys were conducted in the U.S. These surveys provide valuable descriptive information of the senior population at specific points in time, which will subsequently be incorporated into the methodology developed. Table 3.1 illustrates the year/s cross sectional/longitudinal surveys (of interest in this study) which were conducted with respect to the year 2000/2001. Table 3.1 Dataset Year of Survey DATASET 1998 1999 2000 2001 2002 Decennial Census Health and Retirement Study National Household Survey 3.7 Future Year The year 2030 is taken to be the future year of estimate. Estimates of the number of seniors in the year 2030 have been generated by the U.S. Bureau of Census at both national and state levels. Table 3.2 presents national figures.

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46 Table 3.2 2030 Population Estimates by Cohort (in ’000s) Cohort 2000 (Jul 1) 2030 (Jul 1) Proportion of Total 2000 (%) Proportion of Total 2030 (%) % Year on Year increase from 2000 65 69 yrs 9,533 19,980 3.4 5.5 2.50 70 74 yrs 8,849 17,967 3.1 4.9 2.39 75 79 yrs 7,425 13,988 2.6 3.8 2.13 80 84 yrs 4,984 9,913 1.8 2.7 2.32 85yrs+ 4,267 9,603 1.5 2.6 2.74 65yrs+ 35,061 71,453 12.4 19.6 2.40 Total Population 282,125 363,584 100.0 100.0 0.85 Source: U.S. Interim Projections by Age, Sex, Race, and Hispanic Origin 2000 – 2050, U.S. Census Bureau, Population Divi sion, Population Pr ojections Branch The year 2030 is expected to have 10.4 percent of the U.S. population between 65 – 74 years old, currently the 2nd highest (behind 2029) sub-cohort proportion of any Decennial Census year projected by the U.S. Census Bureau of senior persons ages 65 – 74 (up to the year 2050 based on 2000 census projections). In addition, if the cohorts 65 to 74 years and 75 to 84 years are taken together, this also peaks (at 17 percent of the total population) in the year 2030. Figure 3.1 graphically presents information regarding projected proportions of the senior population and it becomes evident that, with each passing decade from 2000, persons aged 65 years and older will represent a greater proportion of the total U.S. population. 3.8 Driver Licensing Rates and Numbers of Drivers Earlier, in section 2.7, the closing gap of licensure rates by gender (a phenomenon of the late 20th century) was discussed. Concomitant with successive waves of seniors in future decades, there will not only be a greater number of seniors but more of them will be licensed at levels never witnessed before in U.S. driver licensing history. In the year 2000, 92 percent of males over the age of 65 in the U.S. were licensed, compared to 68 percent of females, a difference of 24 percentage points (Office of

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47 Highway Policy Information 2001; U.S. Census Bureau 2000). Nevertheless, how will the predicted licensing levels in 2030 be reflected in the actual numbers of licensed seniors? A first step to predicting licensing levels in 2030 is to revisit the licensing levels of persons ages 35 years+ in 2000, as depicted in Tables 3.3 and 3.4. Table 3.3 Licensing Proportions (Males) in 2000 Population/ Licensed 35 – 39 yrs (65 – 69yrs in 2030) 40 – 44 yrs (70 – 74yrs in 2030) 45 – 49 yrs (75 – 79yrs in 2030) 50 – 54 yrs (80 – 84yrs in 2030) 55+ yrs (85+ yrs in 2030) Population 2000* 11,276,70411,168,6599,955, 8678,706,148 26,170,474 # Licensed* 10,621,91010,576,9769,578, 2688,448,424 24,626,777 % Licensed* 94.19%94.70%96.21%97.04% 94.10% *Note: Figures are for cohorts in 2000. Sources: Office of Highway Policy Information, 2001 & U. S. Census Bureau (2004) Projected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) Population Division, Popul ation Projections Branch 0% 5% 10% 15% 20% 25% 200020102020203020402050% Total Population 65 to 74yrs 75 to 84yrs 85yrs+ 65yrs+ Figure 3.1 Senior Population Cohorts of Total Population 2000 – 2050 Source: U.S. Census Bureau (2004) U.S. Inte rim Projections by Age, Sex, Race, and Hispanic Origin 2000 2050, Population Division, Popul ation Projections Branch

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48 Table 3.4 Licensing Proportions (Females) in 2000 Population/ Licensed 35 – 39 yrs (65 – 69yrs in 2030) 40 – 44 yrs (70 – 74yrs in 2030) 45 – 49 yrs (75 – 79yrs in 2030) 50 – 54 yrs (80 – 84yrs in 2030) 55+ yrs (85+ yrs in 2030) Population 2000* 11,339,80211,353,88310,270, 5589,083,519 33,314,509 # Licensed* 10,437,549 10,516,251 9,575,36 3 8,419,527 25,374,152 % Licensed* 92.04%92.62%93.23%92.69% 76.17% *Note: Figures are for cohorts in 2000. Sources: Office of Highway Policy Information, 2001 & U. S. Census Bureau (2004), Projected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) Population Division, Popul ation Projections Branch Tables 3.3 and 3.4 present predicted licensing levels for males and females, respectively, for the year 2030. These statistics were derived from population and licensing levels in the year 2000. Persons reaching “senior” status in 2030 (i.e., 65 years and older) would have been at least 35 years old in 2000. Thus, estimated licensing levels and populations in 2030 for persons between 35 to 39 years, 40 to 44 years etc., are presented in Tables 3.5 and 3.6 for males and females, respectively. Table 3.5 Predicted Number of License Holders (Males) in 2030 Population/ Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85+ yrs Estimated Population 2030 9,473,1048,280,8246,159, 6574,089,194 3,339,937 Predicted % Licensed* 94.19%94.70%96.21%97.04% 94.10% Predicted # Licensed 8,923,0387,842,1305,926, 0383,968,144 3,142,927 Note: Estimated percentages based on Table 3.3 Source: U.S. Census Bureau (2004) Projected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) Population Division, Population Projections Branch Table 3.6 Predicted Number of License Holders (Females) in 2030 Population/ Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85+ yrs Estimated Population 2030 10,507,1589,686,8477,829, 2495,824,404 6,263,097 Predicted % Licensed* 92.04%92.62%93.23%92.69% 76.17% Predicted # Licensed 9,671,1548,972,2017,299, 3015,398,649 4,770,317 Note: Estimated percentages based on Table 3.4 Source: U.S. Census Bureau (2004) Projected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) Population Division, Population Projections Branch

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49 It is evident that gender licensing rates for these cohorts are similar (especially for the youngest-old), a development which has matu red concomitantly with the greater participation of women in the economy/workforce. The cohort progression of licensing levels is illustrated in Table 3.7. Applying the percentages presented in Tables 3.3 and 3.4 to the cohort population estimates for 2030, an estimate as to the numbers of licensed drivers can be developed. These estimates may be optimistic but as Burkhardt et al. (1998, p. 37) noted in deriving their projections of future drivers, “there is no set of number with a solid research foundation to estimate, with confidence, the levels of future driving of our oldest citizens.” 55yrs+ in 2000 2000 % Licensed 2010 % Licensed 2020 % Licensed 2030 % Licensed Cohort/ Year 35 39yrs 40 44yrs 45 49yrs 50 54yrs 55 59yrs* 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ M 94.2 F 92.0 M 94.7 F 92.6 M 96.2 F 93.2 M 97.0 F 92.7 M 94.0 F 76.2 M 94.2 F 92.0 M 94.7 F 92.6 M 96.2 F 93.2 M 97.0 F 92.7 M 94.0 F 76.2 M 94.2 F 92.0 M 94.7 F 92.6 M 96.2 F 93.2 M 97.0 F 92.7 M 94.0 F 76.2 M 94.2 F 92.0 M 94.7 F 92.6 M 96.2 F 93.2 M 97.0 F 92.7 M 94.0 F 76.2 60 64 y rs Table 3.7 Licensure Rates and Cohort Projections

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50 Despite this, a number of caveats can be made in the interpretation of the estimates, as follows: The figures represent only an estimate based on historical data relationships. It is assumed that non-licensed immigrants (35 years and older) coming to the U.S. over the next few decades will acquire licensing status similar to that of their respective age cohorts. Research has shown that acquiring a license is one of several demonstrable factors indicating a ssimilation into the American lifestyle enabling travel patterns similar to the U.S. born population (Myers, 1996). The estimated figures may not represent all senior persons licensed or driving in 2030. Nevertheless, for persons who were licensed in 2000 and will be alive in 2030, these estimates can be taken to represent seniors in 2030 who, at some earlier stage in their lives, were licensed and t hus had the ability to drive at that point in time. Driver licenses may be personally held (i.e., for identification purposes), but the holder may not actually drive. As noted by Levy (1995, p. 461), “Not all drivers are legally licensed and not everyone who is licensed actually drives.” The projections for 2030 of the future numbers of senior drivers by Burkhardt et al. (1998) representing their worst case scenario (i.e., equivalent equal licensing rates for men and women plus 5%t) are shown in Tables 3.8 and 3.9. These estimates are also compared with those derived in the present study (i.e., Tables 3.5 and 3.6).

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51 Table 3.8 Comparison of Predicted Number of License Holders (Males) in 2030 Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Predicted # Licensed 8,923,0387,842,1305,926, 0383,968,144 3,142,927 Predicted # Licensed* 9,670,0348,295,2525,703, 0613,229,855 1,783,165 Difference 7.72%5.46%-3.91%-22.86% -76.26% Burkhardt et al, 1998, Table 2-8, pp. 34 Table 3.9 Comparison of Predicted Number of License Holders (Females) in 2030 Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Predicted # Licensed 9,671,1548,972,2017,299, 3015,398,649 4,770,317 Predicted # Licensed* 10,325,5809,369,5407,096, 1434,699,513 3,799,921 Difference 6.34%4.24%-2.86%-14.88% -25.54% Burkhardt et al, 1998, Table 2-8, pp. 34 As can be seen, differences between the estimates become wider with each advance in age cohort. Nevertheless, if all senior cohorts are combined, the resulting estimates are strikingly close (i.e., differences of -3.91% and -2.33% for males and females, respectively), an unintended result but nonetheless interesting. However, the largest differences are seen in estimates for the 85 years and older cohort. These differences may be due to Burkhardt et al. using licensing rates and population estimates from 1996, whereas this study uses licensing rate s and revised population estimates (with significant changes to the “oldest old,” 85 year plus cohort) based on the 2000 census (published 2005). Based on their projections in the number of drivers, Burkhardt et al. (1998) did noted the potentially significant increase in t he number of senior licensed drivers as at the time of their study and the future. Using figures derived from this research, Tables 3.10 and 3.11 depict the change in the numbers of senior licensed drivers (males and females). Senior female licensed drivers, in particular, are responsible for the greatest percentage increases in each of the age cohorts identified. Despite the many

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52 uncertainties of the future, Burkhardt et al. (1998, p. 38) stated that, “given the absence of significant change to societal patterns of personal mobility, particularly as they affect our eldest citizens, the number of older licensed drivers will at least double in the next 35 years.” The estimates produced here confirm this statement. Table 3.10 Numbers of License Holders (Males) in 2000 and 2030 Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Actual # Licensed 2000 4,182,933 3,644,990 2,820, 136 1,656,789 957,463 Predicted # Licensed* 8,923,0387,842,1305,926, 0383,968,144 3,142,927 Difference % 113.32%115.15%110.13%139.51% 228.26% Note: Predicted # Licensed based on Table 3.5 Source: Office of Highway Policy Information Table 3.11 Numbers of License Holders (Females) in 2000 and 2030 Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Actual # Licensed 2000 4,202,950 3,822,570 3,091,01 3 1,854,278 1,092,687 Predicted # Licensed* 9,671,1548,972,2017,299, 3015,398,649 4,770,317 Difference % 130.10%134.72%136.15%191.15% 336.57% Note: Predicted # Licensed based on Table 3.6 Source: Office of Highway Policy Information 3.9 Driving Cessation Several methodologies have been put forward to estimate the number of persons transitioning to driving cessation. Interrogation of longitudinal and cross sectional datasets is one method; the use of multi-state life tables is another. In the case of multistate life tables, using a synthetic cohort, transition probabilities are derived for each stage of driving, i.e., driving, reduced driving and stopped driving. “As such, the multistate life table allows us to derive the proportion of older people in each driving status state at each age and the expected time to be spent in each state” (Waldorf & Pitfield,

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53 2005 p. 79). Nevertheless, the same articles goes on to state that “data necessary to estimate the transition probabilities do not exist” (Waldorf & Pitfield 2005, p. 79). 3.9.1 Estimates of Driving Cessation Wallace Much has already been stated about the process of driving cessation (see previous chapter) but, as to a possible scenario in 2030, four methods are applied to estimates of licensed drivers in 2030. The first approach is that developed by Wallace (Eberhard 1996) using the 1993/1994 Assets and Health Dynamics Survey (AHE AD), incorporated into Wave #2 of the HRS dataset (see secti on 3.4.1). In this study, Wallace derived proportions of former drivers ages 70 years or more, presented in Table 3.12. Former drivers would be persons who declared themselv es as drivers (i.e., not only licensed but driving) during earlier waves (i.e., Wave 1 in 1992) of the survey but at a later survey stage declared that they did no longer drive (identification of this progression is made possible through longitudinal surveys). Table 3.12 also indicates that, in all age cohorts, there are greater proportions of female former drivers than males as well as an exponential increase in these proportions as age increases. Table 3.12 Proportion of Senior Former Drivers (Percent Stopped Driving) Gender 70 74 yrs 75 79yrs 80 84 yrs 85yrs + Male 10 14 21 43 Female 17 23 35 52 Source: Wallace (Eberhard, 1996) Applying the percentages of former drivers as derived by Wallace (Table 3.12) to the estimates of seniors licensed to drive in 2030, it is possible to obtain an estimate as to

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54 the number of former drivers at this future year. These estimates are presented in Table 3.13. 3.9.2 Estimates of Driving Cessation – Foley et al. The second methodology used to derive numbers of former drivers is that developed by Foley et al. (2002) from research estimating t he “Driving Life Expectancy” of seniors. Foley and colleagues interrogated 1993 and 1995 data from the AHEAD dataset. The sample analyzed (4,699 persons) consisted of persons ages 70 years and older. At the baseline (1993), respondents were asked if they were able to drive and had a car available. Again in 1995, to follow up, the same question was asked to those of the original sample who were still living. Weights were applied to the sample enabling national representation, to account for mortality, non-response and driver status (active driver or former driver). Interpreting the results on a national scale Foley et al. found that, over the two year period of follow-up, seven percent of the 13.7 million drivers (70 years and older) died and, of those who survived, nine percent (1.2 million) ceased driving. Thus, over one year, approximately 620, 000 senior drivers aged 70 years and above (i.e., 428,232 males + 811,167 females / 2) transitioned to become former drivers. The results are presented in Tables 3.14 and 3.15.

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55 Table 3.13 Predicted Drivers and Former Drivers 2030 (Based on Wallace (Eberhard, 1996)) 70 – 74yrs 75 – 79yrs 80 – 84yrs Licensed MalesFemalesMalesFemalesMales Females Predicted # Licensed* 7,842,1308,972,2015,926,0387 ,299,3013,968,144 5,398,649 Predicted % Former Drivers** 10.0%17.0%14.0%23.0%21.0% 35.0% # Predicted Former Drivers 784,2131,525,274829,6451, 678,839833,310 1,889,527 Predicted Actual Drivers 7,057,9177,446,9265,096,3925 ,620,4623,134,834 3,509,122 *See Tables 3.7 & 3.8 **Wallace (see Eberhard, 1996) 85+ yrs Total Licensed MalesFemalesMalesFemales Predicted # Licensed* 3,142,9274,770,31720,879,23826,440,468 Predicted % Former Drivers** 43.0%52.0%18.2%28.6% # Predicted Former Drivers 1,351,4592,480,5653,798,6277,574,205 Predicted Actual Drivers 1,791,4682,289,75217,080,61118,866,262 *See Tables 3.7 & 3.8 **Wallace (see Eberhard, 1996) 55

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56 Table 3.14 Prevalence of Driving and Not Driving for Males (1993 – 1995) Age Cohort AHEAD Sample Size Estimated # Drivers U.S. Population* % Drivers % Stopped Driving # Stopped Driving 70-74 1,017 2,969,0003,372,00088.02.9 86,101.0 75-79 683 2,036,0002,431,00084.46.2 126,232.0 80-84 443 1,081,0001,385,00078.111.2 121,072.0 85+ 187 433,000793,00054.621.9 94,827.0 Total 2,330 6,519,0007,981,000 428,232 Source: Foley et al. (2002) *AHEAD weighted population of community-dwelling elderly persons Table 3.15 Prevalence of Driving and Not Driving for Females (1993 – 1995) Age Cohort AHEAD Sample Size Estimated # Drivers U.S. Population* % Drivers % Stopped Driving # Stopped Driving 70-74 1,077 3,288,0004,710,00069.85.9 193,992.0 75-79 726 2,196,0003,633,00060.411.0 241,560.0 80-84 412 1,212,0002,707,00044.819.3 233,916.0 85+ 154 447,0002,015,00022.231.7 141,699.0 Total 2,369 7,143,00013,065,000 811,167 Source: Foley et al. (2002) *AHEAD weighted population of community-dwelling elderly persons The former driver percentages (i.e., percent stopped driving), as indicated in Tables 3.14 and 3.15 are of importance here. They indicate the wide disparity between genders with respect to the prevalence of driving cessation, i.e., female drivers were more likely to cease from driving in all the age cohorts presented. In addition, the data indicate the exponential increase in the percentage of ceased drivers with increasing age. Accepting the two year cessation percentages indicated in Tables 3.14 and 3.15 and applying them to the estimates of seniors licensed to drive in 2030, it is possible to obtain a second estimate as to the number of former driver s in this future year. These estimates are presented in Table 3.16. 3.9.3 Estimates of Driving Cessation – Waldorf A third method to estimate the numbers of fo rmer drivers is that developed by Waldorf (2001), who looked at anticipated mode choices following driving cessation. In the

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57 Table 3.16 Predicted Drivers and Former Drivers 2030 (Based on Foley et al.) 70 – 74yrs 75 – 79yrs 80 – 84yrs Licensed MalesFemalesMalesFemalesMalesFemales Predicted # Licensed* 7,842,1308,972,2015,926,0387 ,299,3013,968,1445,398,649 Predicted % Former Drivers** 2.9%5.9%6.2%11.0%11.2%19.3% # Predicted Former Drivers 227,422529,360367,414802,923444,4321,041,939 Predicted Actual Drivers 7,614,7088,442,8415,558,6246 ,496,3783,523,7124,356,710 85+ yrs Total Licensed MalesFemalesMalesFemales Predicted # Licensed* 3,142,9274,770,31720,879,23926,440,468 Predicted % Former Drivers** 21.9%31.7%8.3%14.7% # Predicted Former Drivers 688,3011,512,1901,727,5693,886,413 Predicted Actual Drivers 2,454,6263,258,12719,151,67022,554,055 57

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58 course of this study, Waldorf was also able to derive cessation estimates based on Eberhard’s (1996) paper. These estimates were a synthesis of FHWA licensing rates and the prevalence of active drivers as derived from the 1993/1994 AHEAD study. Waldorf determined that the proportion of persons ceasing from driving can be represented by the equation: cl cd cl cd elp p p p p p 1 where *prepresents persons who have stopped driving, elp proportion ever licensed, clpproportion currently licensed, and cdpproportion currently driving (i.e., active drivers). Three driving cessation scenarios were further developed by Waldorf (see Appendix B); Scenario 1 represents the case where the proportion of persons ever licensed equaled the proportion currently licensed; Scenario 2 assumed universal licensing, that is elp = 1; and Scenario 3 was the average of scenarios 1 and 2. It was found that this latter scenario (Scenario 3) gave a more realistic driving cessation estimate. Driver cessation probabilities from Scenario 3 are presented in Tables 3.17 and 3.18. Estimates from Scenario 3 are then applied to driver licensing estimates 2030 and results are presented in Table 3.19. Table 3.19 presents the estimated number of licensed drivers, driving cessation proportions (as determined by Waldorf 2001) and estimates of the number of actual versus ceased drivers in 2030.

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59 Table 3.17 Driving Cessation Estimates for Older Persons in the USA (Males)*** Scenarios 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ *Proportion currently licensed, pcl 0.940.910.87 0.75 Base Case **Proportion currently driving, pcd 0.880.850.77 0.54 Proportion ever-licensed, pel = ( pcl + 1) 0.970.960.94 0.88 Scenario 3 Proportion stopped driving, p* 0.090.110.18 0.38 Sources: OHPI/FHWA, ** AHEAD, ***Eberhard 1996 in Waldorf 2001 Table 3.18 Driving Cessation Estimates for Older Persons in the USA (Females)*** Scenarios 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ *Proportion currently licensed, pcl 0.740.640.49 0.26 Base Case *Proportion currently driving, pcd 0.700.600.44 0.22 **Proportion everlicensed, pel = ( pcl + 1) 0.870.820.75 0.63 Scenario 3 Proportion stopped driving, p* 0.200.270.41 0.65 Sources: OHPI/FHWA, ** AHEAD, ***Eberhard 1996 in Waldorf 2001 3.9.4 Estimates of Driving Cessation – Waldorf and Pitfield Another more recent methodology that has the potential to estimate the number of persons experiencing the driving cessation process is the use of multi-state life tables. Here, using a synthetic cohort, transition probabilities are derived for each stage of driving, i.e., driving, reduced driving and stopped/ceased driving. “As such, the multistate life table allows us to derive, the proportion of older people in each driving status state at each age and the expected time to be spent in each state” (Waldorf & Pitfield 2005, p. 79). Nevertheless, the same article goes on to state that “the data necessary to estimate the transition probabilities do not exist.” The Life Table approach to estimating t he numbers of former drivers is an application developed by Waldorf & Pitfield (2005). A life table is defined as “a statistical table that follows a hypothetical cohort of 100,000 persons born at the same time as they progress through successive ages, with the cohort reduced from one age to the next

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60 Table 3.19 Predicted Drivers and Former Drivers 2030 (Based on Waldorf) 70 – 74yrs 75 – 79yrs 80 – 84yrs Licensed MalesFemalesMalesFemalesMalesFemales Predicted # Licensed* 7,842,1308,972,2015,926,0387 ,299,3013,968,1445,398,649 Predicted % Former Drivers** 9.0%20.0%11.0%27.0%18.0%41.0% # Predicted Former Drivers 705,7921,794,440651,8641, 970,811714,2662,213,446 Predicted Actual Drivers 7,136,3387,177,7615,274,1745 ,328,4903,253,8783,185,203 85+ yrs Total Licensed Males Females Males Females Predicted # Licensed* 3,142,9274,770,31720,879,23926,440,468 Predicted % Former Drivers** 38.0%65.00%15.6%34.3% # Predicted Former Drivers 1,194,3123,100,7063,266,2349,079,404 Predicted Actual Drivers 1,948,6151,669,61117,613,00517,361,064 60

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61 according to a set of death rates by age until all persons eventually die” (U.S. Census Bureau 1996). A life table thus defined is technically referred to as a “Period” Life Table (i.e., synthetic population) versus a “Cohort” Life table, which follows the life experience of an actual birth cohort. In addition, a life table can be “abridged” (i.e., data grouped by 5 or 10 year age intervals) or “complete” (i.e., data for individual years). Life tablesfor the U.S. are produced annually by the National Center for Health Statistics (NCHS) a unit of the Centers for Disease Control (CDC). The complete life table for males and females in 2000 (as published by the NCHS) is presented in Appendix C. The creation of an abridged life table for persons 35 years and older (base year 2000) is described as follows. The year 2000 was set as the base year in this study with 2030 as the forecast year. As life tables for future years have not been published by the NCHS, life tables (male and female) for the year 2000 will form the platform to derive probabilities of survival to the year 2030. Life tables generated for the year 2000 are presented in Tables 3.20 and 3.21, for 35 year old males and females respectively. Appendix D presents the detailed methodology followed in the derivation of these life tables. As the focus of the study is the year 2030, to derive the number of senior former drivers in this year it is necessary to determine the proportion of persons alive (xS) at least 30 years post 2000 (i.e., the opposite of mortality probabilities). In other words having reached 35 years or more in 2000 what is the probability of reaching 65 years or more in 2030 (based on the 2000 life tables). In this particular case, the probability is derived by dividing the cumulative number of deaths between a cohort (i.e., x ) and 30 years hence (i.e.,30 x) by the number surviving to age x (i.e.,xl) in 2000, as given in the formula:

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62 Table 3.20 Life Table for Males: United States 35yrs+, 2000* Cohort Population (xl) Prob’ Dying (xq) Deaths (xd) Person Years Lived (xL) Person Years Lived Total (xT) Life Expectancy (xe) 35 39yrs 100,000 0.010257 1,026497,4364,135,932 41.36 40 44yrs 98,974 0.0151261, 497491,1293,638,496 36.76 45 49yrs 97,477 0.0224072, 184481,9263,147,368 32.29 50 54yrs 95,293 0.0312502, 978469,0212,665,442 27.97 55 59yrs 92,315 0.0479774, 429450,5032,196,421 23.79 60 64yrs 87,886 0.0735576, 465423,2691,745,918 19.87 65 69yrs 81,422 0.1114549, 075384,4211,322,648 16.24 70 74yrs 72,347 0.16827712,174331,298938,228 12.97 75 79yrs 60,172 0.24870814,965263,449606,929 10.09 80 84yrs 45,207 0.36743816,611184,508343,480 7.60 85 89yrs 28,596 0.53416415,275104,794158,972 5.56 90 94yrs 13,321 0.7200439,59242,62654,178 4.07 95 99yrs 3,729 0.8804943,28410,43811,552 3.10 100yrs+ 446 1.0000004461,1141,114 2.50 for detailed methodology used in deriving table see Appendix D Table 3.21 Life Table for Females: United States 35yrs+, 2000* Cohort Population (xl) Prob’ Dying (xq) Deaths (xd) Person Years Lived (xL) Person Years Lived Total (xT) Life Expectancy (xe) 35 39yrs 100,000 0.005667567498,5834,584,834 45.85 40 44yrs 99,433 0.008600855495,0294,086,250 41.10 45 49yrs 98,578 0.0124391,226489,8263,591,222 36.43 50 54yrs 97,352 0.0186671,817482,2173,101,396 31.86 55 59yrs 95,535 0.0297582,843470,5662,619,179 27.42 60 64yrs 92,692 0.0472844,383452,5022,148,613 23.18 65 69yrs 88,309 0.0730476,451425,4181,696,111 19.21 70 74yrs 81,858 0.1107779,068386,6221,270,692 15.52 75 79yrs 72,790 0.17175612,502332,696884,071 12.15 80 84yrs 60,288 0.27047716,307260,674551,374 9.15 85 89yrs 43,982 0.42474718,681173,205290,700 6.61 90 94yrs 25,301 0.63226115,99786,511117,495 4.64 95 99yrs 9,304 0.8339787,75927,12230,983 3.33 100yrs+ 1,545 1.0000001,5453,8623,862 2.50 for detailed methodology used in deriving table see Appendix D

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63 x l l xl dx x301 where, xdcumulative number of deaths between year xl and30 xl, and xlnumber of survivors at age x years. For example, the probabilit y of a females ages 40 to 44 years in 2000 (n= 99,433 see Table 3.21) surviving 30 years to see their 70th to 74th year in 2030 (n= 81,858 see Table 3.21) is 0.823248., i.e., 82 percent of females ages 40 to 44 years in 2000 are estimated to live to see their 70th to 74th year in 2030 (based on 2000 life tables). The resulting survival probability estimates for males and females for the year 2030 are presented in Table 3.22. Table 3.22 Male and Female Survivor Probabilities xS (65 Years and Older) Males Females Age 2000 Age 2030 Prob of Dying (xq) Prob of Surviving (xS) Prob of Dying (xq) Prob of Surviving (xS) 35 39yrs 65 69yrs 0.1857850.8142150.116910 0.883090 40 44yrs 70 74yrs 0.2690350.7309650.176752 0.823248 45 49yrs 75 79yrs 0.3827020.6172980.261598 0.738402 50 54yrs 80 84yrs 0.5255990.4744010.380720 0.619280 55 59yrs 85 89yrs 0.6902320.3097680.539627 0.460373 60 64yrs 90 94yrs 0.8484270.1515730.727047 0.272953 65 69yrs 95 99yrs 0.9541970.0458030.894643 0.105357 70 74yrs 100yrs+ 0.9938400.0061600.981130 0.018870 Waldorf & Pitfield (2005) also produced five-year (assumed) cessation probabilities for seniors. These cessation probabilities adhere to the following criteria, namely; increase with age and are greater for women than for men.2 The cessation probabilities are presented in Table 3.23. 2 Personal communication with Brigitte Waldorf

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64 Table 3.23 Five-Year (Assumed) Cessation Probabilities for Seniors Age Cohort Men Women 65 to 69 0.05 0.05 70 to 74 0.05 0.10 75 to 79 0.10 0.20 80 to 84 0.10 0.20 85 to 89 0.40 0.80 90 to 94 0.50 0.99 95+ 1.00 1.00 Source: Waldorf & Pitfield (2005, p. 80) The cessation probabilities are applied to the probability of dying (xq) for each particular cohort, producing revised xq*. The “probability of dying forms the basis of the life table.” (Arias 2002, p. 2) The revised probability of dying results in changes to life table calculations, subsequently producing new survival probabilities. Appendix E gives the detail surrounding the derivation ofxq*, and xS*. Table 3.24 presents the probability of survival xSto the year 2030 (derived from the regular life table) and the probability for surviving and actively driving xS*. The resulting probability curves are illustrated in Figures 3.2 and 3.3. Table 3.24 Male and Female Survivor (xS) and Surviving & Driving (xS*) Probabilities Males Females Age 2000 Age 2030 Prob of Surviving (xS) Prob of Surviving & Driving (xS*) Prob of Surviving (xS) Prob of Surviving & Driving (xS*) 35 39yrs 65 69yrs 0.8142150.8142150.883090 0.883090 40 44yrs 70 74yrs 0.7309650.6944170.823248 0.782086 45 49yrs 75 79yrs 0.6172980.5571110.738402 0.631333 50 54yrs 80 84yrs 0.4744010.3853320.619280 0.423588 55 59yrs 85 89yrs 0.3097680.2264480.460373 0.251916 60 64yrs 90 94yrs 0.1515730.0664820.272953 0.029872 65 69yrs 95 99yrs 0.0458030.0100450.105357 0.000115 70 74yrs 100yrs+ 0.0061600.0000000.018870 0.000000

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65 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 65 69yrs 70 74yrs 75 79yrs 80 84yrs 85 89yrs 90 94yrs 95 99yrs 100yrs+Female Probability Prob' Surviving (Sx) Prob' Surviving & Driving (S*x) Figure 3.3 Survivor Curves xSand xS*(Females) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 65 69yrs 70 74yrs 75 79yrs 80 84yrs 85 89yrs 90 94yrs 95 99yrs 100yrs+Male Probability Prob' Surviving (Sx) Prob' Surviving & Driving (S*x) Figure 3.2 Survivor Curves xSand xS* (Males)

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66 Figures 3.2 and 3.3 indicate that for senior men ages 65 years or more in 2030, the median age (where p = 0.5) of survival xS is 79 years, with a driving life expectancy xS* of 77 years. This is in comparison to the median life expectancy xS for senior women in 2030 approximating 84 years with a corresponding xS* of 78 years. The difference between the survivor probability curve (xS) and the surviving and driving curve (xS*) at a specific probability represents the number of years during which a person is in need of assistance with transportation, i.e., they become former drivers and may seek alternative non-personally operated transportation modes. The final stage in order to derive the numbers of persons transitioning to the former driver status according to Waldor f & Pitfield is calculated from the following formula (Waldorf & Pitfield 2005, p. 82): ) ( ) (*t P S S S t Nx n x x x x n where ) (t Nx nis the number of persons in need of non-personally operated automobile transportation, xS survivor probability, xS*, driving life expectancy, and ) (t Px n is the size of the age cohort. Applying this method, the estimated numbers of males and females in need of non-automobile transportation in 2030 are presented in Tables 3.25 and 3.26. (Note: (1) the driver population figures are based on licensed driver proportions in 2000 (i.e., 30 years before the cohort in question, for example, the cohort 65 to 69 yrs in 2030 is based on the cohort 35 to 39 years in 2000, and (2) as survivor probabilities have been given to age 100+ the es timates of licensed drivers 85yrs+ have been re-calculated – see Appendix F for details)).

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67 Table 3.25 Estimated Former Driver Population in 2030 (Males) Cohort (Males) xS xS* Driver Population # Ceased Driving ) (t Nx n 65 to 69yrs 0.81420.81428,923,038 0 70 to 74yrs 0.73100.69447,842,130 392,106 75 to 79yrs 0.61730.55715,926,038 577,789 80 to 84yrs 0.47440.38533,968,144 745,019 *85yrs+ 0.30980.00003,234,826 1,337,218 see Appendix F for recalculati on of driver population 85yrs+ Table 3.26 Estimated Former Driver Population in 2030 (Females) Cohort (Females) xS xS* Driver Population # Ceased Driving ) (t Nx n 65 to 69yrs 0.88310.88319,671,154 0 70 to 74yrs 0.82320.78218,972,201 448,610 75 to 79yrs 0.73840.63137,299,301 1,058,399 80 to 84yrs 0.61930.42365,398,649 1,705,973 *85yrs+ 0.46040.00005,506,872 3,644,796 see Appendix F for recalculati on of driver population 85yrs+ The ceased driving estimates presented in Tables 3.25 and 3.26 do illustrate the higher number of female former drivers when com pared to males. The cohort 85 to 89 years witnesses a rise in the numbers of former drivers for both males and females. However, this is related to the steep change in the assumed cessation probabilities presented in Table 3.23. Overall, the figures indicate that in 2030 approximately 15 percent of the senior driving population (i.e., 65 years and older) will have transitioned to the former driver state and thus have need of other non-personally operated transportation modes. 3.10 Driving Cessation Caveats Three primary caveats can be made in the interpretation of the driving cessation estimates developed, presented in the following sections.

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68 3.10.1 Cumulative Cessation Rates Over Time In section 2.4, it was noted that a recent estimate of the numbers of senior drivers who retire from driving annually approximated 1 million (Staplin & Freund 2005). However, from the four methods assessed in this chapter, the driving cessation estimates are to be taken as cumulative as at the year 2030. Indeed, it can be seen that, with each advance in age cohort, the proportion of former drivers increases. In other words, taking Wallace’s estimates (see Table 3.13), in 2030, 10 percent of senior male drivers in the 70 to 74 year cohort will have retired from dr iving, as at 31 December (correspondingly 17 percent of senior female drivers), some of these would have been before 2030 and some during the same year. However, if former drivers from the 70 to 74 age cohort survive to the next cohort (75 – 79 years), they will be added to the numbers of former drivers in this cohort who were active drivers in their previous cohort. Thus, as age increases, there is an expansion in the proportion of former drivers of the number of total senior drivers with a specific age cohort. 3.10.2 Gender Differences in Cessation Rates In an overwhelming number of studies, female cessation rates are higher for males in the same age cohort. Suggested reasons for the differences in cessation rates by gender have stated that it is partly due to the greater importance given to driving by elderly men when compared to women. On the other hand, for elderly women, having a living spouse who prefers to drive can relieve them from the necessity to drive. However, according to the study by Foley et al. (2002), the lack of correcting for the differing mortality rates between men and women biases cessation rates in favor of men. This error is evidenced when studies of senior former drivers do not assess those drivers who were actively driving at the point of their deaths, i.e., the cessation rate is based on

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69 former drivers who survive after giving up dr iving. Thus, “when mortality risk is factored into the rates of cessation (more men will stop due to death than women over time), the risk of driving cessation is comparable and therefore results in similar driving life expectancies [i.e., cessation rates].”3 This conclusion by Foley et al. (2002) is unique among studies of driving cessation as it goes against the trend of widely differing cessation rates by gender. To incorporate the reasoning by Foley et al. a sensitivity test (presented in Chapter 4) will be carried out where the derivation of transit market size will allow for female cessation rates that are equal to that of males. 3.10.3 Cessation Rates of the 65 to 69 Year Cohort In three of the cessation estimate methods assessed, driving cessation rates for the 65 – 69 year cohort were not derived. This was not necessarily due to the lack of drivers in this age cohort transitioning to former driver status, but due to the use of the AHEAD dataset in the estimation of former drivers. As indicated in section 3.12, the AHEAD dataset only includes senior persons ages 70 years or more. Thus, it would not be possible using this dataset to derive cessation estimates for seniors younger than 70 years. 3.11 Derivation of Potential Transit Market Size To estimate the potential transit market size in the forecast year of 2030, the approach to be followed is that of applying transit trip rates derived from the NHTS 2001 to the numbers of non-drivers (i.e., former driv ers and persons who have never driven). However, before this is done, an understanding of elderly travel behavior is described. 3 Personal communication with Daniel Foley

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70 3.12 Travel Behavior of the Elderly To better understand the short and long distance travel behavior of Americans, the Bureau of Transportation Statistics (BTS) of the U.S. Department of Transportation (USDOT), FHWA and NHTSA have intermittently, since 1969, collected detailed data on personal travel. The NPTS, as it was referred to in 1969, has been collected in 1977, 1983, 1990, 1995 and 2001. The 2001 survey was conducted over the period of March 2001 to May 2002 and covered all 50 states. The goal of the NHTS is to create a national inventory of daily and long distance travel. Such information is useful for policy makers and strategic planners in the course of defining the safety, quality, and efficiency of the U.S. transportation system. The primary method of collecting data was via the Computer Aided Telephone Interview (CATI) from a sample of the non-institutionalized population. CATIs were facilitated using the Random Digit Dialing Me thod (RDD). The survey process included household interviews, in-person interviews, and odometer readings from personal motor vehicles. Interviewees and their resp ective trip-making behavior came from households/persons representing all socio-economic backgrounds, ethnic groups, and ages (including, for the first time, children ages 0 to 4 years old). Each respondent was asked to give details of all trips made (both local and long distance but not international) on a particular travel day. Over 106,000 household interviews were conducted, and approximately 163,000 person interviews were completed. Of the 106,000 households interviewed, approximately 70,000 households provided usable information. In the case of person interviews, 161,000 were usable. The unweighted person response rate (in households where at least half of the adults completed the person interview) was approximately 60 percent for both the full sample and the national sample. The overall unweighted survey

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71 response rate was 29 percent and 37 percent of the full sample and national sample, respectively. Refer to the 2001 National Househ old Travel Survey User’s Guide (USDOT 2004) for more information on the survey and weight estimation methodology and response rates. The 2001 NHTS dataset contains four separate files: household, person, vehicle and travel day trips. The household file contains information relating to each household, e.g., number of vehicles; the person file contains information relating to each person, e.g., age, race, etc.; the vehicle file contains information relating to the household vehicle(s), e.g., vehicle type; and the travel day trip file contains information detailing each trip made on the household’s randomly-a ssigned travel day. Details (i.e., number of records) of each file and weighted sums are presented in Table 3.27. Table 3.27 National Household Travel Survey 2001 Data File Statistics Data File Sample Size (# of records) Weighted Sum Household (unit = households) 69,817 107,365,346 Person (unit = perso ns) 160,758 277,203,235 Vehicle (unit = vehi cles) 139,382 202,586,200 Travel day person trips (unit = trips)642,292 407,262,485,207 Source: National Househol d Travel Survey 2001 3.13 Dataset Caveats The following caveats are given in interpretation of the 2001 NHTS dataset. 3.13.1 Cross Sectional Versus Longitudinal Datasets Despite data being collected over a number of years, the NHTS is not a longitudinal survey as the travel behaviors of the sa me persons have not been followed in each of the survey years. Thus, “within-person” change, i.e., how does each person change over time, and “inter-individual” change, i.e., what predicts differences among people in their changes, cannot be derived from analyses of NHTS datasets. Indeed, cross

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72 sectional datasets over time compare different individuals at one point in time with another set of individuals at another point of time and try to draw individual inferences about change over time. Such shortcomings of cross sectional datasets (and the benefits of longitudinal research) are investigated further in Singer & Willett (2003). 3.13.2 Institutionalized Populations According to the 2001 NHTS Users Guide, “An eligible household excludes telephones in motels, hotels, group quarters, such as nursing homes, prisons, barracks, convents or monasteries and any living quarters with 10 or more unrelated roommates” (USDOT, 2004 p. 1-7). Thus, transit behavior of seniors resident in such institutions cannot be presented in this discussion. According to the 2000 Decennial Census, approximately 444,000 males ages 65 and over were institutionalized in 2000, compared to 1.2 million senior women. Of these figures, approximately 400,000 senior males resided in nursing homes compared to 1.1 million senior females. It is evident that for senior males and females, residing in nursing homes represented 90 percent and 96 percent, respectively, of the institutionalized populations. Figure 3.4 presents the proportions of institutionalized seniors of the total senior population. 3.13.3 Transportation Definitions Two general transportation mode definitions are assessed in the following sections, POVs and transit. A total of 26 transportation modes are incorporated into the NHTS 2001. With respect to POV transportation, this is defined to include car, van, sport utility vehicle (SUV), pickup or other truck, recreational vehicle (RV), and motorcycle. Transit (public transportation)is defined as local public transit bus, commuter bus, city to city bus, commuter train, subway/elevated rail and street car/trolley.

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73 3.13.4 Driving/Licensure Status4 Data in the NHTS 2001 indicate the driving status of the respondent rather than their driving licensure status. In executing the survey, the respondent is simply asked, “Are you a driver?” The answer can be either “Yes ” or “No.” Nothing is asked about whether the respondent holds a current driving license. Thus, if the respondent has an expired license but is still driving, they are recorded as a “driver.” On the other hand, if a respondent has a current license but does not drive (i.e., due to a health impairment) they are a ‘non-driver.’ 4 Based on email communication with Nancy McGuckin and Nanda Srinivasan 11/04/2006 0% 1% 2% 3% 4% 5% 6% 7% MalesFemalesPercent of Total Senior (65yrs+) Population Institutionalized Nursing Homes Figure 3.4 Percent of Senior Population Residing in Institutions 2000 Source: U.S. Census Bureau (2005)

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74 3.13.5 Elderly Demographics The U.S. population approximated 285,107,923 persons, according to Census estimates for the year 2001, of which of 35,329,850 (12%) were age 65 years or older. Population estimates for the same year computed from the NHTS survey (using the person file) approximated 277,203,235 persons, of which 32,884,068 (12%) were 65 years and older. Figure 3.5 illustrates Census and NHTS estimates of the population by age cohort in 2001 and the difference between the two estimates (with Census estimate as base). It is evident that, at the oldest-old age cohorts, the differences are significant, with NHTS estimates being approximately 35 percent less than the estimates derived from the Census for persons 85 years and older. This may be partially attributable to the Figure 3.5 Population Estimates by Senior Age Cohort (Year 2001) Sources: NHTS 2001 & U.S. Census Bureau (2004) Pr ojected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) Populat ion Division, Populati on Projections Branch 0 2,000 4,000 6,000 8,000 10,000 12,000 65-69yrs70-74yrs75-79yrs80-84yrs85yrs+Population ('000)-60% -50% -40% -30% -20% -10% 0% 10%Percent Difference NHTS Census Difference

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75 fact that NHTS does not include institutionalized persons (discussed earlier) in the database as well as to the sample size of the “oldest old” senior population. 3.14 Transit Trip Rates As the focus of this research is deriving the future demand for transit, an understanding of daily transit use of the population is required. It is anticipated that these rates will be applied to the senior population in the year 2030 to estimate the transit market size. Thus, this section will look at transit trip use from a variety of perspectives. First, Market Assessment #1, the overall use of transit by seniors will be presented. 3.14.1 Transit Trip Rates – Market Assessment #1: General Population Transit trip rates for the senior population (approximately 33 million persons) as derived from the NHTS 2001 are presented in Table 3.28. It is evident that daily transit trip rate is negligible for the senior population, when compared to the daily trip rate (all trips). Table 3.28 Transit Trip Rates Market Assessment #1: General Population Market Assessment #1 Trips PopulationDaily Trip Rate 2001 Total Trips 0 64yrs 366,272,055,294244,319,1674.11 2001 Total Trips 65yrs+ 40,990,429,91332,884,0683.42 2001 Transit Trips 0 64yrs 6,149,312,016244,319,1670.07 2001 Transit Trips 65yrs+ 503,068,68332,884,0680.04 Source: NHTS 2001 Person and Trip Files 3.14.2 Transit Trip Rates – Market Assessment #2: Urban/Rural Population Table 3.29 presents data with respect to transit trip rates broken down by whether the trip maker was located in an urban versus rural location. According to the NHTS 2001, of the 6.6 billion trips made by transit, 6.5 billion trips (98%) were made in urban areas. This severe disparity possibly contributes to the zero daily transit trip rate for seniors as depicted in Table 3.29. Indeed, the lack of transit use by seniors residing in rural locations is primarily due to the non-availability of transit services in such areas.

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76 Table 3.29 Transit Trip Rates Market Assessment #2: Urban/Rural Population* Market Assessment #2 Trips Urban Population Daily Trip Rate Trips Rural Population Daily Trip Rate 2001 Total Trips 0 64yrs 289,645,261,201 190, 950,308 4.16 76,626,79 4,127 53,368,861 3.93 2001 Total Trips 65yrs+ 32,434,626,485 25,622,499 3.47 8,555,8 03,425 7,261,571 3.23 2001 Transit Trips 0 64yrs 6,018,647,645 190,950,308 0.09 130,664, 372 53,368,861 0.01 2001 Transit Trips 65yrs+ 500,341,685 25,622,499 0.05 2,726, 998 7,261,571 0.00 Table 3.30 Transit Trip Rates Market Assessment #3: Urban Driver/Non-Driver Population* Market Assessment #3 Trips Active Driver Population Dail y Trip Rate Trips Non-Driver Population Daily Trip Rate 2001 Total Trips 0 64yrs 213,313,223,561 127,113, 550 4.60 76,332,037, 616 63,836,759 3.28 2001 Total Trips 65yrs+ 29,216,362,781 19,89 2,925 4.02 3,218,263, 711 5,729,575 1.54 2001 Transit Trips 0 64yrs 2,935,343,743 127,113, 550 0.08 3,083,303,92 2 63,836,759 0.13 2001 Transit Trips 65yrs+ 159,513,500 19,892,925 0.02 340,828,189 5,729,575 0.16 Table 3.31 Transit Trip Rates Market Assessment #4: Urban Non-Driving Population and Household Driver Availability* Market Assessment #4 Trips Zero Drivers in Household Population Daily Trip Rate Trips Driver in Household Population Daily Trip Rate 2001 Total Trips 0 64yrs 5,392,524,661 5,358,8 08 2.76 70,939,512,96 2 58,477,956 3.32 2001 Total Trips 65yrs+ 1,343,355,866 2,348, 859 1.57 1,874,907, 841 3,380,717 1.52 2001 Transit Trips 0 64yrs 1,433,165,576 5,358,8 08 0.73 1,650,138,34 0 58,477,956 0.08 2001 Transit Trips 65yrs+ 257,148,542 2,348,859 0.30 83,679,644 3,380,717 0.07 Table 3.32 Transit Trip Rates Market Assessment #5: Urban Non-Driving Population and Household Vehicle Availability* Market Assessment #5 Trips Zero Vehicles in Household Population Daily Trip Rate Trips Vehicle in Household Population Daily Trip Rate 2001 Total Trips 0 64yrs 7,658,773,577 7,436,0 26 2.82 68,673,264,04 9 56,400,733 3.34 2001 Total Trips 65yrs+ 1,353,076,756 2,376, 609 1.56 1,865,186, 948 3,352,962 1.52 2001 Transit Trips 0 64yrs 1,801,485,576 7,436,0 26 0.66 1,281,818,34 4 56,400,733 0.06 2001 Transit Trips 65yrs+ 284,969,789 2,376,609 0.33 55,858,397 3,352,962 0.05 Tables 3.29 to 3.32 data source: NHTS 2001 Person and Trip Files 76

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77 Thus, in light of the dominance of transit trip making in the urban areas, ongoing analysis of the NHTS with respect to senior trav el behavior will focus on transit use in urban areas only. 3.14.3 Transit Trip Rates – Market Assessment #3: Urban Driver/Non-Driver Population Table 3.30 presents data with respect to transit use according to the driving status of seniors residing in an urban area. Driver status is a significant factor contributing to the number of out-of-home trips made. Indeed, of the 25 million seniors residing in urban areas, approximately 20 million (78%) described themselves as “drivers” (i.e., actively driving) and had a daily trip rate significant ly higher than those seniors who were not drivers. 3.14.4 Transit Trip Rates – Market Assessment #4 Urban Non-Driving Population and Household Driver Availability Market Assessment #4 as presented in Table 3.31 presents trip rate data for seniors residing in households with or without a driver present in that household. It is evident that the lack of a driver in the household is a contributing factor to the percentage of transit trips made of all out-of-home trips. 3.14.5 Transit Trip Rates – Market Assessment #5 Urban Non-Driving Population and Household Vehicle Availability Market Assessment #5 (as shown in Table 3.32) presents data on trip rates according to household vehicle availability. It is evident (in the majority of cases) that the lack of a vehicle for a non-driving senior has a greater impact on the daily trip rate than the lack of another household driver (compare Table 3.30).

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78 3.14.6 Summary Trip Frequency Behavior Summarizing the various trip rates contained in the scenarios above is shown in the form of a trip rate tree as indicated in Figure 3.6. As can be seen, senior non-drivers have a daily trip rate that is 50 percent less than senior drivers. Indeed, the highest trip rate for seniors is in respect of those seniors residing in urban areas who drive. Figure 3.6 Senior Population Daily Trip Rate Tree Source: Author’s analysis of the NHTS 2001 All Trips 3.42 Trips/Day Rural Urban Active Driver Non-Driver Zero Vehicles in Household Driver/s in Household 3.47 Trips/Day 3.23 Trips/Day 1.54 Trips/Day 4.02 Trips/Day Vehicle/s in Household Zero Drivers in Household 1.57 Trips/Day 1.52 Trips/Day 1.56 Trips/Day 1.52 Trips/Day

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79 CHAPTER 4 – RESULTS AND DISCUSSION 4.1 Introduction Chapter 3 presented the methodology developed to estimate the number of former drivers in the year 2030. This current chapter will discuss the results of the estimation process by way of presenting: estimates of the senior population in 2030 according to their driving licensure status; estimates of current and former senior drivers in the year 2030; and estimates of transit market size in 2030 through the application of daily trip rates to the senior population according to their licensure and driving status. Sensitivity tests will also be conducted with the estimates of current and former drivers in 2030. In addition, there will be a discussion of the active and former driver estimates taking note of factors that may influence the size of the future senior transit market, as discussed in Chapter 2. 4.2 Estimates of the Senior Driving Population in 2030 Estimates of the numbers of seniors according to their licensure status were reviewed in section 3.8. Tables 4.1 and 4.2 revisit these estimates by presenting the estimated numbers of licensed and unlicensed seniors in the year 2030.

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80 Table 4.1 Estimated Senior Population by Licensure Status (Males) 2030 Population/ # Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85+ yrs Estimated Population 2030 9,473,1048,280,8246,159, 6574,089,194 3,339,937 Predicted # Licensed 8,923,0387,842,1305,926, 0383,968,144 3,142,927 Predicted # Unlicensed 550,066438,694233,619121,050 197,010 Note: Estimated percentages based on Table 3.3 Source: Projected Population of the United States, by Age an d Sex: 2000 to 2050 (Detailed Table) U.S. Census Bureau, Population Division, Popul ation Projections Branch Table 4.2 Estimated Senior Population by Licensure Status (Females) 2030 Population/ # Licensed 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs 85+ yrs Estimated Population 2030 10,507,1589,686,8477,829, 2495,824,404 6,263,097 Predicted # Licensed 9,671,1548,972,2017,299, 3015,398,649 4,770,317 Predicted # Unlicensed 836,004714,646529,948425,755 1,492,780 Note: Estimated percentages based on Table 3.4 Source: Projected Population of the United States, by Age an d Sex: 2000 to 2050 (Detailed Table) U.S. Census Bureau, Population Division, Popul ation Projections Branch Of the 31 million males in 2030, it is estimated that the overall licensing rate for this group will be approximately 95 percent, compared to 90 percent of the 41 million senior women. 4.3 Active and Former Drivers The estimated numbers of seniors that may have ceased driving by 2030 or may be going through the driving cessation process during that year, according to the four different methods, are presented in Table 4.3 and displayed graphically in Figures 4.1 and 4.2. The estimates range from a conservative 5.6 million senior former drivers in 2030 (aged 70 years and older), as per the method developed by Foley et al. (2002), to a high of 12.3 million seniors applying Waldorf’s (2001) method. Another observation arising from the

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81 Table 4.3 Estimated Senior Former Driver Population 2030 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs Males Females Males Females Males Females Males Females Predicted # Licensed 8,923,038 9,671,154 7,842,130 8,972,201 5,926,038 7,299,301 3,968,144 5,398,649 # Former Drivers Wallace na na 784,213 1,525,274 829,645 1,678,839 833,310 1,889,527 # Former Drivers Foley na na 227,422 529,360 367,414 802,923 444,432 1,041,939 # Former Drivers Waldorf na na 705,792 1,794,440 651,864 1,970,811 714,266 2,213,446 # Former Drivers Waldorf & Pitfield* 0 0 392,106 448,610 577,789 1,058,399 745,019 1,705,973 na = not assessed licensing rates for 85yrs+ recalc ulated (see Appendix F for details) 85+ yrs Total Males Females Males Females Predicted # Licensed 3,142,927 4,770,317 29,802,277 36,111,622 # Former Drivers Wallace 1,351,459 2,480,565 3,798,627 7,574,205 # Former Drivers Foley 688,301 1,512,190 1, 727,569 3,886,412 # Former Drivers Waldorf 1,194,312 3,100,706 3,266,234 9,079,403 # Former Drivers Waldorf & Pitfield* 1,337,218 3,644,796 3,052,132 6,857,778 81

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82 Figure 4.1 Estimated Male Former Drivers 2030 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 70 74yrs75 79yrs80 84yrs85yrs+Thousands # Former Drivers Wallace Foley et al. Waldorf Waldorf & Pitfield Figure 4.2 Estimated Female Former Drivers 2030 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 70 74yrs75 79yrs80 84yrs85yrs+Thousands # Former Drivers Wallace Foley et al. Waldorf Waldorf & Pitfield

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83 estimated driving cessation results is that an upward trend in the numbers of former drivers is evident for both males and females. Furthermore, the steepest rise or fall in cohort estimates for both genders is evident at 85 years and older. This result is supportive of evidence that suggests that during the ninth decade of life (colloquially referred to as the “Decade of Reckoning”) there is a greater chance that personal health status will be compromised through chronic i llness for example, impacting driving skills and capability5. In all age cohorts, the propensity to transition to become a former driver was greater for women than men (even when taking into account the four distinct estimation methods). Wallace’s method produced the lowest overall average of 2 female former drivers ( 70 years) to every 1 male former dr iver (7.5 million / 3.7 million; see Table 4.3), when compared to 2.78 using Waldorf’s (2001) method. These driving cessation ratios are significantly higher than the corresponding licensing ratio of 1.26 senior female drivers ( 70 years) for every 1 senior male driver in 2030. In addition, only one method, namely Waldorf & Pitfield (2005), estimated fo rmer drivers ages 65 to 69 years. It is accepted that many seniors in this age bra cket continue to drive without any health or other problems impacting their driving capability. However, the lack of estimates for the senior population ages between 65 to 69 years using the other three methods (Wallace (Eberhard, 1996), Foley et al. 2002, and Waldorf, 2001) is due to the use of the AHEAD longitudinal dataset, which only includes persons 70 years and older in the sample. In Spring 2005, Staplin & Freund estimated that 1 million senior drivers (70 years and older) ceased driving annually. Based on 2004 licensing data, at that time the senior driver licensed population ( 70yrs) approximated 20 million persons or, put another way, 8 out of 10 senior persons were licensed (population 70 years in 2004 5 Personal communication with Daniel J Foley 05/12/06

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84 approximated 26 million). From Staplin & Freund’s estimates, approximately 1 in 20 senior drivers ages 70 years and older stopped driving in 2004/2005. Similarly, the estimates for 2030 indicate a former driver ( 70 years) ratio of between 1 in 8 (according to Foley et al. 2002) to 1 in 4 (Waldorf 2001). Overall, the four methods assessed in this study predict that in 2030, 12 to 26 percent of seniors ( 70 years) holding driver licenses may have ceased driving and be in need of non-personally operated transportation modes in order to maintain their mobility and activity levels precessation. Looking at the cessation estimates for persons in their ninth decade of life, there seems to be a greater clustering of estimates for males (around 1.2 million, in Figure 4.1) when compared to the wider disparity for females (ranging from a low of 1.5 million to 3.8 million, in Figure 4.2). Historically, the higher incidence of licensed male drivers has contributed to the gender disparity in former driver estimates. The extent of such disparities being perpetuated into the future (taking into account the closing gender gap in driver licensing), confirm the challenge of predicting the licensing and corresponding driving behavior of senior females in the future. Many of the relationships that have been developed to assess senior driving behavior and cessation are based on empirical/historical relationships which are unlikely to be repeated in the future due to the increasing incidence of licensed seniors. There has never been a time where licensing rates of senior women have been similar to that of senior males (i.e., licensed in excess of 90 percent with a corresponding gender difference 5 percentage points). Nevertheless, this scenario is currently evident for persons aged 35 to 64 years (see Tables 3.3 and 3.4) and will impact senior age cohorts with each advancing decade post 2000. Burkhardt et al. (1998, p.28) noted in their study regarding predicting senior women driving behavior, “will they [women born

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85 after 1950] exhibit the driving behavior of those women now in the 65 and above age group or will they drive more like they, themselves, did in their 20s and 30s?” Concomitantly, with respect to this study, will senior women in the coming decades cease driving as senior women do now [i.e., in 2005/2006] or follow closely the driving behavior and cessation patterns of senior males in that future year? The far larger number of persons ages 65+ in future decades provides a larger group of licensed senior drivers, many of w hom will deal with driving cessation sometime during their lives. The number of persons who will face driving cessation is expected to increase dramatically and in absolute terms provide a large group of individuals who will have to transition to alternative mobility options. Public policy may very well influence not only the size of the group that transitions but the public sector’s response. Such a response may include proactive involvement in offering mobility choices through public venues and numerous additional pubic actions such as service coordination, education and counseling to former drivers. Figure 4.3 provides a summary of the estimated current and future driving status of the senior population. The large growth in seniors and licensure rates results in far more seniors who are expected to transition from driving but fewer unlicensed drivers. 4.4 Never Driven In all cohorts in 2030, there will be those seniors who may have never acquired a driver’s license (permitting them to drive l egally) in their lifetimes. The actual numbers maybe small (taking note of the decreasing gender differences in licensing in recent decades; see section 2.7); nevertheless, this group must also be added to the numbers of former drivers. Tables 4.4 and 4.5 present the total number of persons that may seek

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86 alternatives to the automobile in the year 2030, favorable (base) and worse case scenarios. 4.5 Driving Cessation Favorable Versus Worse Case Analysis In determining the transit market share attributable to seniors in the forecast year, it will be necessary to include those seniors who may be using transit for the first time (or after a long time since their pre-driving period) post cessation. In the subsequent analysis it is anticipated that the numbers of former s enior drivers in future years will follow the favorable (base) case scenario as assessed by Foley et al. (2002). Assumptions governing this anticipated scenario in 2030 are as follows: Figure 4.3 Estimated Current and Future Senior Drivers According to Driving Status (70 Years and Older) 0 10,000 20,000 30,000 40,000 50,000 60,000 20042030 Base Case2030 Worst CaseThousands Senior Population 70yrs+ Senior Active Drivers Senior Former Drivers Senior Unlicensed

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87 Table 4.4 Estimated Senior Former Driver Population 2030 (Favorable Case – Foley et al., 2002) 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs Cohort Males Females Males Females Males Females Males Females Population 9,473,104 10,507,158 8,280,824 9,686,847 6,159,657 7,829,249 4,089,194 5,824,404 Predicted # Licensed 8,923,038 9,671,154 7,842,130 8,972,201 5,926,038 7,299,301 3,968,144 5,398,649 #Never Driven 550,066 836,004 438,694 714,646 233,619 529,948 121,050 425,755 #Former Drivers 0 0 227,422 529,360 367,414 802,923 444,432 1,041,939 #Total Non-Drivers 550,066 836,004 666,116 1,244,006 601,033 1,332,871 565,482 1,467,694 Non Drivers % of Cohort Population 5.81% 7.96% 8.04% 12.84% 9.76% 17.02% 13.83% 25.20% 85+ yrs Total Cohort Males Females Males Females Population 3,339,937 6,263,097 31,342,716 40,110,755 Predicted # Licensed 3,142,927 4,770,317 29,802,277 36,111,622 #Never Driven 197,010 1,492,780 1,540,439 3,999,133 #Former Drivers 688,301 1,512,190 1,727,569 3,886,412 #Total Non-Drivers 885,311 3,004,970 3,268,008 7,885,545 Non Drivers % of Cohort Population 26.51% 47.98% 10.43% 19.66% 87

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88 Table 4.5 Estimated Senior Former Driver Population 2030 (Worse Case – Waldorf, 2001 ) 65 – 69yrs 70 – 74yrs 75 – 79yrs 80 – 84yrs Cohort Males Females Males Females Males Females Males Females Population 9,473,104 10,507,158 8,280,824 9,686,847 6,159,657 7,829,249 4,089,194 5,824,404 Predicted # Licensed 8,923,038 9,671,154 7,842,130 8,972,201 5,926,038 7,299,301 3,968,144 5,398,649 #Never Driven 550,066 836,004 438,694 714,646 233,619 529,948 121,050 425,755 #Former Drivers 0 0 705,792 1,794,440 651,864 1,970,811 714,266 2,213,446 #Total Non-Drivers 550,066 836,004 1,144,486 2,509,086 885,483 2,500,759 835,316 2,639,201 Non Drivers % of Cohort Population 5.81% 7.96% 13.82% 25.90% 14.38% 31.94% 20.43% 45.31% 85+ yrs Total Cohort Males Females Males Females Population 3,339,937 6,263,097 31,342,716 40,110,755 Predicted # Licensed 3,142,927 4,770,317 29,802,277 36,111,622 #Never Driven 197,010 1,492,780 1,540,439 3,999,133 #Former Drivers 1,194,312 3,100,706 3,266,234 9,079,403 #Total Non-Drivers 1,391,322 4,593,486 4,806,673 13,078,536 Non Drivers % of Cohort Population 41.66% 73.34% 15.34% 32.61% 88

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89 Licensing rates of seniors (for both males and females) in all likelihood will be above 90 percent, in particular for persons ages 64 to 84 years. Concomitantly, at these licensing proportions, it is also likely that males and females will have similar driving histories (i.e., length of time actively driv ing). Longer driving histories, leading to a increased familiarity with and competency of driving and a greater dependency on driving in meeting transportation needs, t he incidence of cessation in all likelihood will be less than it is for seniors today (2006) and the incidence of transitioning to former driver status similar for both senior males and females. So the lower estimates of driving cessation using the method of Foley et al. (2002) is preferable. Life expectancy at birth has been increasing over recent decades, where, in 2003, life expectancy for males approximated 74. 8 years and females 80.1 years (Arias 2006, p. 3). With respect to males, increased life expectancy in future years may result in more men living to report driv ing cessation rather than being omitted from the driving cessation equation due to their deaths while actively driving (see section 3.10.2). This needs to be coupled with senior females with driving habits similar to their male counterparts. Therefore, both of these factors have the potential to close the driving cessation gender gap, which also supports lower estimates of driving cessation as according to Foley et al. (2002). 4.6 Transit Use by Seniors Evidence from the National Household Travel Survey 2001 Results from analysis of the NHTS 2001 are presented in this section. It is noted that public transportation (transit) may represent a transportation alternative for seniors, if certain conditions are met. National transpor tation mode choice statistics as derived from the NHTS are presented in Table 4.6. It is evident that the use of transit in 2001 for

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90 Table 4.6 Daily Travel by Mode (Billion Trips in 2001) in U.S.A. National 19-64yrs 65yrs+ Mode # Trips Percent# TripsPercent# Trips Percent POV* 351.8 86.4%236.089.4%36.5 89.0% TRANSIT** 6.7 1.6%4.81.8%0.5 1.2% WALK 35.4 8.7%19.97.5%3.5 8.5% BIKE 3.3 0.8%1.20.5%0.1 0.3% OTHER 10.2 2.5%2.20.9%0.4 0.9% TOTAL 407.3 100.0% 264. 1100.0% 41.0 100.0% *POV Includes car, van, sport utility v ehicle (SUV), pickup or other truck, re creational vehicle (RV), or motorcycle **Transit includes local public transit bus commuter bus, city to city bus, commuter train, subway/elevated rail and street car/trolley Source: NHTS 2001 Trip File persons ages 65 years and older represented only 1 2 percent of trips; traveling in POV travel was by far the overwhelming and preferred choice of transportation. Figure 4.4 presents percentage of trips made by transit by age cohort. Even though, in all senior age cohorts, transit trip use is minimal, overall there is an upward trend, particularly in the oldest-old cohorts. This upward trend may be due to widowhood (with its concomitant financial implications) and the increasing desire of seniors in the oldest-old age cohorts to remain mobile despite limitat ions brought on by socio-economics or age. The desire to remain mobile may lead seniors to experiment in using public transportation. 4.6.1 Gender and Transit Use If gender is taken into account with respect to transit use by seniors, the following is evident as presented in Tables 4.7 and 4.8. Senior women make 1.7 percent of trips by transit compared to 0.7 percent of trips by senior men. In fact, for every 1 transit trip by a senior male, a corresponding 2.5 transit trips were made by senior women in 2001. The greater use of transit by senior women may be partly due to widowhood (and the unavailability of another person facilitate POV transportation) and lower licensure rates

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91 for older females. Evidence supporting this conclusion is indicated through analysis of driving life expectancy versus life expectancy. As women live longer than men, they will have longer periods (after driving cessation) in need of alternatives to POV transportation. Table 4.7 Modal Split for Daily Travel in U.S.A. (Billion of Trips Males Year 2001) Mode National Percent 19-64yrs Perc ent 65yrs+ Percent POV* 171.3 86.4%113.889.7%17.6 90.3% TRANSIT** 3.0 1.5%2.11.7%0.1 0.7% WALK 16.5 8.3%9.07.1%1.5 7.7% BIKE 2.2 1.1%0.80.6%0.1 0.5% OTHER 5.3 2.7%1.20.9%0.2 0.8% TOTAL 198.3 100.0%127.0100.0%19.4 100.0% Source: NHTS 2001 Trip File Figure 4.4 Percent Transit Trips by Senior Age Cohort (Year 2001) Source: NHTS 2001 Trip File 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8% 2.0% 65-69yrs70-74yrs75-79yrs80-84yrs86yrs+All 65yrs+Percent Transit Trips

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92 Table 4.8 Modal Split for Daily Travel in U.S.A. (Billion of Trips Females Year 2001) Mode National Percent 19-64 yrs Percent 65yrs+ Percent POV* 180.4 86.4%123.489.0%18.9 87.9% TRANSIT** 3.7 1.8%2.72.0%0.4 1.7% WALK 18.9 9.0%11.08.0%2.0 9.3% BIKE 1.1 0.5%0.40.3%0.0 0.2% OTHER 4.8 2.3%1.10.8%0.2 1.0% TOTAL 208.9 100.0%138.7100.0%21.5 100.0% Source: NHTS 2001 Trip File Figure 4.5 graphically portrays senior transit use by age and gender. In all age cohorts, senior women utilize transit more than senior men. In fact, for senior women there is evidence of a strong positive correlation between transit use and age. Figure 4.5 Percent Transit Trips by Senior Age Cohort and Gender (Year 2001) Source: NHTS 2001 Trip File 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 65-69yrs70-74yrs75-79yrs80-84yrs85yrs+65yrs+Percent Transit Trips Males Females

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93 4.6.2 Minorities and Transit Use Chapter 2 discussed various factors that have positively influenced transit use (and may continue to in forthcoming decades), one of which was minority (ethnic) status. Figure 4.6 presents the proportion of all trips made by transit according to age cohort and ethnic status. It is evident that minorities in the age cohorts depicted in Figure 4.6 utilized transit to a higher extent (of all trips made) than whites. With respect to seniors, approximately 11 percent of trips made by Hi spanic seniors were made by transit; this was double the proportion of the next closet ethnic group, that of African American/ Black at 5 percent. A significant percentage of ethnic seniors reside in central cities, which, in many cases, have associated tr ansit services. Residential location and socioeconomics are major contributory factors to the higher utilization rate of transit by ethnic 0% 2% 4% 6% 8% 10% 12% WhiteAfrican American/Black AsianHispanicPercent Transit Trips 0-18yrs 19-64yrs 65yrs+ Figure 4.6 Percent Transit Trips by Senior Age Cohort and Ethnicity (Year 2001) Source: NHTS 2001 Trip File

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94 seniors. With the ethnic proportion of the total U.S. population predicted to grow rapidly in future years, if current transit utilization trends are continued, this may have positive benefits for transit agencies through increased ridership levels. 4.6.3 Modal/Market Share by Age Cohort An understanding of modal/market share by age cohort may enable a cursory determination of the market size for each mode, i.e., transit, with respect to the proportion of all trips made on each particular mode. Modal/market share can then be compared with the respective proportion of total trips and trips on a particular mode that each cohort represents. Through such a comparison, the level of dependency or nondependency on a particular mode can be gauged. Such relationships are relevant in the current study, as changes in population proportions (e.g., the significant growth in the 65+ year cohort) will have corresponding impacts in modal shares. Figure 4.7 presents modal/market share proportions by age cohort for all trips made on a particular mode. The mode/market share dominance of the 19 to 64 years cohort is evident in Figure 4.7, particularly for POV, transit and walking modes. Of the modes depicted in Figure 4.7, only for the bike mode did the 19 to 64 years age cohort not contribute to the majority of bike trips. Indeed, for POV and transit trips, the 19 to 64 years cohort contribution to these trips exceeded their population share (61 percent in 2001), noting that the senior population proportion in 2001 was estimated at 12 percent. For all the modes depicted in Figure 3.9, the senior contribution to the trips on each particular mode was less than their proportion of the population, only POV came in close at 10 percent.

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95 With respect to transit, the NHTS estimated that over 6 billion trips were made using transit in 2001. It is evident from Figure 4.7 that persons ages 65 years and older were responsible for 7.7 percent of transit mode share, approximately 5 percentage points less than their estimated proportion of 2001 total population. The dominance of transit use for work related trips has influenced the negatively disproportionate share of transit trips made by seniors. This result is to be expected as for the majority of seniors (many of whom are retired), the need for making the work trip is significantly reduced and when transit is used it is to fulfill other trip purposes (discussed further in section 4.6.6). 0% 10% 20% 30% 40% 50% 60% 70% 80% POVTRANSITWALKBIKE Travel ModeMode Share Percent 19 64yrs 65yrs+ Figure 4.7 Mode/Market Share by Age Cohort Source: NHTS 2001 Trip File

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96 In Figure 4.8, this takes the mode/market share proportions as indicated in Figure 4.7 and categorizes them according to the urban and rural location of the respondent’s household. With respect for POV transportation, the results are similar to those presented in Figure 4.7, i.e., approximately 10 percent of the POV trips in either an urban or rural category were made by seniors. However, in the case of transit, trips made by seniors made up 8 percent of the urban transit market share, compared to 2 percent of rural transit market. This difference is primarily due to the denser transit networks available in urban areas when compared to rural areas, resulting in a greater dependency in rural areas on POV transportation. With respect to the walking market 0% 2% 4% 6% 8% 10% 12% POVTRANSITWALKBIKEMode Share Urban Pop' 65yrs+ Rural Pop' 65yrs+ Figure 4.8 Urban and Rural Mode/Market Share by Age Cohort Source: NHTS 2001 Trip File

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97 share, walk trips by seniors accounted for 9 percent of the urban walk trips market share, compared to 4 percent in rural areas. Possible factors influencing the differential in this case may be related to the prevalence of sidewalks in urban areas which permit walking in a safer environment and the greater incidence of longer distances between destinations in rural areas (when compar ed to denser physical environments in urban areas), resulting in a decreased viability of walking as a transportation option for seniors. 4.6.4 Transit Trip Starting Time Another aspect of senior travel behavior is the trip starting time, whether during the day or night of travel. Some senior drivers try to avoid traveling during peak periods, and after dark in evenings and at nights. Figure 4.9 presents start hour of transit trips by age cohort. It is evident from Figure 4.9 that senior transit users exhibit similar behaviors as senior drivers. In the case of senior transit users, the peak trip start hour (14 percent of transit trips) is at midday (12 noon) risi ng steeply from 8am. A secondary peak start hour (10 percent of transit trips) is also evident at 3pm. Both of these peak start hour periods are after (AM Peak) or before (PM Peak) of adults ages 19 to 64 years. As many seniors may be in retirement or in part time work, the need to travel in the AM or PM peak is significantly reduced and many s enior activities are scheduled to avoid peak periods. The concentration of senior transit trips during the off peak hours, (i.e., between the AM and PM peak periods) affords the senior less crowded transit services, enabling one to be seated on the journey but a potential downside of this scenario is that transit service frequency (buses per hour) may also be less. With a potential reduced service frequency and the desire to travel comfortably, the senior has a fixed travel window to complete their daily business. This in it self may limit seniors taking advantage of all

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98 lifestyle opportunities (e.g., shopping or entertainment) in their communities or, on the other hand, discourage senior drivers who are in the process of driving reduction considering transit for non-essential trips. 4.6.5 Transit Trip Starting Time by Age Cohort Another variation of trip starting time is taking into account the proportion of the total number of transit trips made by each age cohort at a particular time. Figure 4.10 presents such information. It is evident that there is no start hour where seniors contribute to the majority transit trips. However, just as 11am represented the hour during which the highest proportion of daily tr ansit trips made by seniors commenced (see Figure 4.9), so too, does 11am represent the hour where daily senior contribution to transit trips is at its highest (18%). Figure 4.9 Distribution of Transit Trips by Start Hour Source: NHTS 2001 Trip File 0% 2% 4% 6% 8% 10% 12% 14% 16% 01234567891011121314151617181920212223 Start HourPercent of Transit Trips National 19 64yrs 65yrs +

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99 4.6.6 Transit Trips and Trip Purpose Given that 1.2 percent of all trips made by seniors in 2001 utilized transit services, what was the reason for initiating the trip? Table 4.9 presents proportion of transit and POV trips according to trip purpose in 2001. Several items of interest are revealed in the data as follows: Of the seniors who used transit in 2001, approximately 7 percent of their transit trips were work-related, whereas 5 percent of their POV trips were work-related. The higher percentage of work-related senior transit trips may be due to socio-economic factors, in that those seniors who used POVs may have been in a better financial position (as evidenced by owning or having immediate access to a car and therefore having less need to work) than those seniors who used transit services. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0 2 4 6 8 10 12 14 16 18 20 22Start HourPercent of Transit Trips by Age Cohort 0-18 19-64 65yrs+ Figure 4.10 Transit Trip Starting Time by Age Cohort Source: NHTS 2001 Trip File

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100 Table 4.9 Distribution of Trips by Trip Purpose, Travel Mode and Age Cohort Transit POV Trip Purpose National 19-64yrs 65yrs+ National 19-64yrs 65yrs+ Work Related 34.9%45.8%7.3%17.8% 24.5% 4.7% Education 11.8%7.6%2.3%7.6% 3.8% 5.6% Medical 5.4%4.6%11.5%2.2% 2.0% 4.6% Shopping 16.0%13.7%38.8%21.7% 21.1% 31.6% Social/Recreation 18.3%15.3%28.2%25.8% 23.4% 27.3% Family/Personal Business 10.8%10.1%11.3%24.1% 24.5% 25.3% Other 2.8%2.9%0.60%0.8% 0.7% 1.0% Total 100.00%100.00%100.00%100. 00% 100.00% 100.00% Source: NHTS 2001 Trip File Excluding work trip purposes, shopping trips represent the largest proportion of trips for both senior POV and transit users. For senior transit users, traveling during the off peak period while increasing the chances of being seated throughout the journey also permits a more amenable environment to be enjoyed when carrying groceries, etc. The high percentage of senior transit shopping trips may also be influenced by limited trip chaining possibilities when access to shopping malls, stores, etc., is dependent on transit service frequency. Thus, instead of visiting three stores during an afternoon, one has to visit one store on each of three days, i.e., one trip chain versus three round trips. The temporal flexibility of POV transportation for seniors is evident in the percentage of trips for family/personal business (25 percent) when compared to senior transit users at 11 percent. This difference may be due to the fact that such trips by POV can be done at anytime, e.g., evenings, specifically during periods where transit services may not be at their best and they may involve other family members with automobile availability. Transit service frequency may also be a factor in the 2 percent of senior transit trips for educational purposes. The attraction of any educational programs for seniors may be eroded if such programs are conducted

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101 during the evenings, which for a number of transit properties is a period with limited transit services and lower service frequencies. Figure 4.11 is a variation of Figure 4.9 and indicates the temporal distribution of senior trip making by three modes. The temporal flexibility of POV transportation and walking (i.e., the peaks are not as pronounced as that of transit) is evident as well as a higher percentage of trips starting after 6pm when compared to transit users. Overall, the higher distribution of trip purpose proportions for senior transit users (e.g., 7.3% transit trips compared to 4.7% POV trips were related to work; see Table 4.9) may not be a reflection as to the actual number of trips. Senior POV drivers and passengers generally make a higher number of daily trips (see Figure 3.6) and this 0% 2% 4% 6% 8% 10% 12% 14% 16%0 2 4 6 8 10 12 14 1 6 1 8 2 0 22Start HourPercent of Trips POV Transit Walk Figure 4.11 Distribution of POV, Transit and Walk Trips by Seniors by Start Hour Source: NHTS 2001 Trip File

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102 will result in lower proportions of trip purposes even when the actual number of trips for a particular trip purpose are the same for transit and POV users. 4.6.7 Transit Trips by Day of Week What day of the week are seniors most likely to use transit when compared to those seniors who utilize POVs? Figure 4.12 illustrates transportation mode used by day of week, of the 503 million transit trips made by seniors in 2001, the distribution of these trips according to day of week. Transit us e by seniors peaks on Mondays; 20 percent of all senior transit trips in 2001 occur on this day. This may be due to engagement in parttime work or the weekly replenishment of groceries after the weekend. Given that weekend transit services are either non-existent or very limited for many transit systems, Monday affords the first opportunity to undertake out of home activities for many seniors, especially where a POV is not available. 0% 5% 10% 15% 20% 25% SunMonTueWedThuFriSatPercent of Trips on mode by Day in 2001 65yrs+ POV 65yrs+ Transit 65yrs+ Bike 65yrs+ Walk Figure 4.12 Daily Distribution of Transportation Mode Used by Seniors Source: NHTS 2001 Trip File

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103 4.6.8 Transit Trip Distance Transit and POV trip distance statistics from the NHTS are shown in Tables 4.10 to 4.12. Overall, the distance traveled per person per day using all modes approximated 40 miles (Table 4.10). This is approximately 4 times the average trip distance (9.75 miles; see Table 4.10) multiplied by the daily trip rate of 4.03 (discussed in section 4.8). However, seniors traveled only 28 miles per day, approximately 40 percent less miles than adults ages 19 to 64 years. In all the three cases, below the average trip length and daily miles traveled for persons over 65 years is shorter than for adults ages 19 to 64 years. This can be expected since, for many seniors, the work commute is no longer a daily occurrence. Table 4.10 Trip Distance Statistics NHTS 2001 All Modes National 19 64yrs 65yrs+ Total Trip Distance (Miles) 3,972,74 8,489,5122,843,388,838,494 336,511,612,527 Total # Trips 407,262,485,209264,129,886,358 40,990,429,912 Average Trip Distance per Trip (Miles) 9.7510.77 8.21 Total Trip Distance (Miles) 3,972,74 8,489,5122,843,388,838,494 336,511,612,527 Total # Persons 277,203,235 163,938,182 32,884,069 Daily Person Miles Traveled 39.2647.52 28.04 Source: NHTS 2001 Trip file Table 4.11 POV Trip Distance Statistics NHTS 2001 Personally Operated Vehicle National 19 64yrs 65yrs+ Total Trip Distance (Miles) 3,519,60 4,279,7102,532,754,238,810 303,181,677,873 Total # Trips 351,755,038,139236,005,474,405 36,498,220,003 Average Trip Distance per Trip (Miles) 10.0110.73 8.31 Total Trip Distance (Miles) 3,519,60 4,279,7102,532,754,238,810 303,181,677,873 Total # Persons 277,203,235 163,938,182 32,884,069 Daily Person Miles Traveled 34.7942.33 25.26 Source: NHTS 2001 Trip file

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104 Table 4.12 Transit Trip Distance Statistics NHTS 2001 Transit National 19 64yrs 65yrs+ Total Trip Distance (Miles) 48,546, 523,13037,974,457,848 2,610,693,720 Total # Trips 6,652,380,6924,751,577,270 503,068,683 Average Trip Distance per Trip (Miles) 7.307.99 5.19 Total Trip Distance (Miles) 48,546, 523,13037,974,457,848 2,610,693,720 Total # Persons 277,203,235 163,938,182 32,884,069 Daily Person Miles Traveled 0.480.63 0.22 Source: NHTS 2001 Trip file Analyzing transit trip distance statisti cs, the average distance traveled per trip approximated 7 miles, 3 miles less than that of POV trips. The dominance of POV travel is evident in the daily person miles travel ed, where transit approximated 0.48 miles per person per day, compared to 35 miles for the POV. 4.6.9 Transit Trip Travel Time Transit and POV travel time statistics from the NHTS are shown in Tables 4.13 to 4.15. Overall, the average time spent per trip using all modes approximated 19.77 minutes (Table 4.13). In all the three cases below, the average travel time per trip for persons over 65 years is shorter than for adults ages 19 to 64 years. Again, as indicated earlier, this can be expected since for many senior s, the work commute is no longer a daily occurrence. Table 4.13 Trip Travel Time Statistics NHTS 2001 All Modes National 19 64yrs 65yrs+ Total Trip Minutes 7,889,770,40 9,4165,294,932,701,740 785,129,947,267 # Trips 407,262,485,209264,129,886,358 40,990,429,912 Average Trip Time per Trip 19.3720.05 19.15 Total Trip Minutes 7,889,770,40 9,4165,294,932,701,740 785,129,947,267 # Persons 277,203,235163,938,182 32,884,069 Daily Person Trip Travel Time (Minutes) 77.9888.49 65.41 Source: NHTS 2001 Trip file

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105 Table 4.14 POV Travel Time Statistics NHTS 2001 Personally Operated Vehicle National 19 64yrs 65yrs+ Total Trip Minutes 6,663,870,25 4,4484,636,772,666,631 684,187,912,601 # Trips 351,755,038,139236,005,474,405 36,498,220,003 Average Trip Time per Trip 18.9419.65 18.75 Total Trip Minutes 6,663,870,25 4,4484,636,772,666,631 684,187,912,601 # Persons 277,203,235163,938,182 32,884,069 Daily Person Trip Travel Time (Minutes) 65.8677.49 57.00 Source: NHTS 2001 Trip file Table 4.15 Transit Travel Time Statistics NHTS 2001 TRANSIT National 19 64yrs 65yrs+ Total Trip Minutes 277,814,513,561207,177,962,535 16,844,961,620 # Trips 6,652,380,6924 ,751,577,270 503,068,683 Average Trip Time per Trip 41.7643.60 33.48 Total Trip Minutes 277,814,513,561207,177,962,535 16,844,961,620 # Persons 277,203,235163,938,182 32,884,069 Daily Person Trip Travel Time (Minutes) 2.753.46 1.40 Source: NHTS 2001 Trip file 4.6.10 Transit Travel and Medical Condition It is acknowledged that “mobility is critical to well-being” (Coughlin & Lacombe 1997, p. 91) and, concomitantly, the level of personal mobility (out of the home) may be related to personal health status. To explore this issue, the NHTS has incorporated questions about medical condition and the ability/desire to undertake out of home travel. Figure 4.13 illustrates the proportion of seniors who have a medical condition that makes travel difficult. As can be seen, female gender and increasing age increases probability of a Medical condition that impacts mobility. Overall, 1 in 5 senior persons had a medical condition which impacted mobility outside of the home.

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106 4.7 Households and Senior Households Understanding the household characteristics of the senior population is paramount in deriving an estimate of the number of seniors that may avail themselves to transit. Research has indicated that “the number one alternative to the car for older adults is not another mode: rather, it is riding with family members and friends” (Coughlin 2001, p. 3). In addition if there is a POV available in a household, along with a licensed driver the desire for senior members to use transit in such a household will be limited. Analysis of the NHTS 2001 Household and Trip files will form the basis of the discussion in this section. Table 4.16 presents data with respect to household size and the proportion of these households with at least one person 65 years and older. Observations from Table 4.16 are as follows: Overall, of the 107 million households, 27 million households 0% 10% 20% 30% 40% 50% 60% 65 69yrs70 74yrs75 79yrs80 84yrs85yrs+65yrs+ Age Cohort% Population with Medical Condition Impacting Mobility Men Women Figure 4.13 Medical Condition Impacting Out-of-Home Mobility Source: NHTS 2001 Person file

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107 Table 4.16 Household Size and Senior Members Household Size All Households Percent of Households # Households with 1P 65yrs+ Percent of Households with 1P 65yrs+ One Person 27,717,61125.82% 10,868,162 39.21% Two Persons 35,032,43332.63% 11,603,291 33.12% Three Persons 17,748,75916.53% 2,403,898 13.54% Four Persons 16,203,07415.09% 958,630 5.92% Five Persons 7,110,6556.62% 538,870 7.58% Six Persons 2,342,2292.18% 212,433 9.07% Seven Persons 703,6450.66% 64,868 9.22% Eight Persons 274,3330.26% 59,645 21.74% Nine Persons 111,7940.10% 4,321 3.87% 10 Persons 68,3310.06% 4,683 6.85% 11 Persons 46,4470.04% 0 0.00% 12 Persons 5,0140.00% 0 0.00% 14 Persons 1,0210.00% 0 0.00% Total 107,365,346 26,718,801 24.89% Source: NHTS 2001 Household file (approximately 25%) had at least one senior member present in 2001. Thirty two percent of all households comprised 2 persons, followed by the 1 person household at 26 percent. Alternatively, of those households where at least one member was 65 years and older, approximately 33 percent of such households were two person households. 4.7.1 Household Population Caveat Analysis of the household file enables each member of a household to be counted, whether or not they were the respondent to the NHTS survey, as each individual in the household is identified in the household record. Identification of each household individual is not possible using the trip file (as some household members may not have made any trips on travel day). Two popul ation figures result from the differing approaches; these are shown in Appendix G. From the household file, an overall population estimate is 274.8 million compared to 277.2 million from the person file. The

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108 reason for this difference is the weighting used with respect to the person and household files of the NHTS. The population calculated from the person file will use weights based at the person level, whereas the population calculated from the household file will use weights at the household level. With respect to the senior population, the estimate from the household file approximated 35,638,862 persons (presented in Table 4.17), slightly more than that of the person file (32,884,069; see Table 4.10). Using population figures from the household file may cause slight differences in resulting trip rate analyses when compared to using population figures from the person file. Thus, for the sake of consistency with published NHTS analyses for trip rate calculations, population figures from the person file will be used. Table 4.17 Senior Population According to Household Size Household Size # Seniors Percent One Person 10,853,71930.52% Two Persons 18,925,45553.21% Three Persons 3,423,0869.62% Four Persons 1,273,4143.58% Five Persons 638,5991.80% Six Persons 279,0300.78% Seven Persons 90,9060.26% Eight Persons 73,9520.21% Nine Persons 4,5490.01% 10 Persons 4,9000.01% 11 Persons 00.00% 12 Persons 00.00% 14 Persons 00.00% Total 35,567,610100.00% Source: NHTS 2001 Household file

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109 4.7.2 Driver or Vehicle Availability by Household Size Analyzing the 27 million households where one or more seniors were present, Table 4.18 presents data relating to these households and the non-availability of an automobile. Table 4.19 presents data relating to the same households according to the non-availability of a driver. From these tables it is evident there is a greater incidence of households without vehicles than that of drivers. Indeed, in the case of senior households the lack of a vehicle may be due to the disposal of a vehicle after driving cessation while still retaining individual licensure status. The cost of maintaining a vehicle (e.g., insurance and general repair), while not driving it can be prohibitive to persons on a fixed income such as a pension. Table 4.18 Households and Senior Households Vehicle Availability All Households Senior Households* Household Size # Households # Households with Zero Vehicles % # Households # Households with Zero Vehicles % One Person 27,717,6095,081,72 918.3%10,868,162 2,839,95426.1% Two Persons 35,032,4301,701,1794.9%11,603,291 525,9554.5% Three Persons 17,748,759709,6574.0%2,403,898 118,7824.9% Four Persons 16,203,076731,0584.5%958,629 87,2979.1% Five Persons 71,10,655322,3014.5%538,870 22,6734.2% Six Persons 23,42,228106,4304.5%212,433 8,7084.1% Seven Persons 703,646 50,8267.2%64,868 3,4125.3% Eight Persons 274,33311,7094.3%59,644 1,4062.4% Nine Persons 111,7936910.6%4,321 00.0% 10 Persons 68,33100.0%4,683 00.0% Total Households 107,312,8608,715,5808.1% 26,718,799 3,608,18713.5% Senior household were at least 1 member is 65 years Source: NHTS 2001 Household file

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110 Table 4.19 Households and Senior Households Driver Availability All Households Senior Households* Household Size # Households # Households with Zero Drivers % # Households # Households with Zero Drivers % One Person 27,717,6113,991,23 914.4%10,868,163 2,535,23523.3% Two Persons 35,032,433901,9012.6%11,603,292 384,9653.3% Three Persons 17,748,759340,0741.9%2,403,898 74,8203.1% Four Persons 16,203,075362,3302.2%958,630 46,3434.8% Five Persons 7,110,655147,2532.1%538,870 2,1850.4% Six Persons 2,342,23052,3642.2%212,433 7,5173.5% Seven Persons 703,64528,5274.1%64,868 00.0% Eight Persons 274,3329,9133.6%59,645 00.0% Nine Persons 111,7941540.1%4,321 00.0% 10 Persons 68,33200.00%4,683 00.0% Total Households 107,312,8665,833,7555.4% 26,718,803 3,051,06511.4% Senior household were at least 1 member is 65 years Source: NHTS 2001 Household file With respect to one person households (the household composition for 30 percent of the senior population), 1 in 4 of such senior households do not have a vehicle available. The incidence of seniors being members in households with zero vehicles or zero drivers decreases with increasing household size. Table 4.20 presents results of the numbers of seniors residing in households with ze ro vehicles or zero drivers. It is evident that approximately 12 percent of t he senior population lived in households where there were zero vehicles, compared to 10 percent of the senior population residing in households where there were zero drivers. These estimates have the potential to indicate those seniors that may be amenable to alternative modes away from the POV. As stated previously, the incidence of seniors living in a household with zero vehicles is greater than that of households with zero drivers, and this is reflected in the actual numbers of seniors in these categories. Estimates from the NHTS 2001 indicate that approximately 4 million seniors lived in households with zero vehicles, representing 12 percent of the total senior population.

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111 Table 4.20 Senior Population in Zero Vehicles or Zero Drivers Available Households Household Size Senior Population in Zero Vehicle Households Senior Population in Zero Driver Households One Person 2,839,9542,535,235 Two Persons 779,286609,632 Three Persons 134,49285,988 Four Persons 110,78150,437 Five Persons 22,6732,185 Six Persons 8,7087,517 Seven Persons 3,4120 Eight Persons 1,406 0 Nine Persons 0 0 10 Persons 0 0 Total 3,900,7123,290,994 % of Senior Population 11.9%10.0% Source: NHTS 2001 Person file 4.7.3 Households and Seniors Only Households As indicated earlier, it is possible from the household file to determine the actual numbers of persons ages 65 and older within each household. As the focus of this research is on the senior individual, it is necessary to separate out households where all members are seniors from households where 1 or more members are seniors. Analysis of the NHTS 2001 (household file) indicated that the maximum number of senior persons in any household equaled 4; however, approximatel y 84 percent of seniors lived in 1 or 2 person households. Table 4.21 presents summary seniors only households statistics. Table 4.21 Household Size Where All Members are Seniors Household Size 1 person 65yrs 2 persons 65yrs 3 persons 65yrs 4 persons 65yrs Total 1 person 10,868,162 (39.2%) 27,717,611 2 persons 7,348,150 (21.0%) 35,032,432 3 persons 90,541 (<1%) 17,748,760 4 persons 5,416 (<1%) 16,203,075 Figures in parenthesis represent the proportion of 100% s enior households of all househol ds in respective size. Source: NHTS Household file

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112 4.8 Trip Frequency Behavior Evidence from the 2001 NHTS indicated that adults 19 to 64 make on average 30 percent more trips per day than adults ages 65 years and older. Table 4.22 indicates the daily person trip rates, where the overall daily trip rate approximated 4 trips per day compared to seniors with 3 trips per day. Table 4.23 presents trip frequency behavior according to gender and age cohort. Table 4.22 Daily Average Number of Trips Cohort # Trips # Persons Average Daily Person Trips 0 – 18yrs 96,193,114,89275,944,038 3.47 19 64yrs 264,129,886,354163,938,182 4.41 65yrs+ 40,990,429,91232,884,068 3.42 All 407,262,485,206277,203,237 4.03 Source: National Household Travel Su rvey 2001(Person and Trip Files) Table 4.23 Daily Average Number of Trips by Gender and Age Cohort Cohort Gender # Trips # Persons Average Daily Person Trips Male 49,403,291,33238,958,298 3.47 0 18yrs Female 46,789,823,56036,985,740 3.47 Male 126,951,752,61380,519,476 4.32 19 64yrs Female 137,178,133,74183,418,706 4.51 Male 19,446,977,25513,898,970 3.83 65yrs+ Female 21,543,452,65718,985,098 3.11 Total 407,262,485,206277,203,237 4.03 Source: National Household Travel Survey 2001(Person and Trip Files) In Table 4.23, it is evident that with each cohort progression the gender difference in the number of daily trips made increases. The higher daily trip rate for females in the 19 – 64 year cohort is possibility due to homemak ing, child rearing and out of home work responsibilities that a growing proportion of women undertake. The notable gender difference in the 65 year plus cohort (3.82 versus 3.11 daily trips for males and females respectively) may be due a continuation of out-of-home activities, e.g., part-time

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113 employment, post-retirement due to active driving status (for males in particular), the lack of a vehicle in the household or the health status of the individual. 4.9 Transit Market Share Results – Market Assessment #1 Market Assessment #1 as initially presented in section 3.21.1 represents transit use by the total senior population. Summarizing transit use facts from the 2001 NHTS it is known that: 503 million transit trips were made by seniors (1.2 percent of all trips made by seniors). Persons ages 65 years and older represented 12 percent of total population. Transit trips made by seniors accounted for 7 percent of the transit market. Deriving an estimate of the transit market share in 2030, the following is assumed: Total number of trips and transit trips for the entire population in 2030 is based on the daily trip and transit trip rates for the total population in 2001 (as derived from the NHTS 2001) multiplied by the population estimates for 2030. Transit trip rates by age cohort and gender for the senior population derived from the 2001 NHTS are applied to the year 2030. Thus, seniors in 2030 are assumed to display similar transit use behaviors as evident in the NHTS 2001. As transit use by seniors (as a percentage of all trips) has been gradually decreasing with each NHTS survey, the application of 2001 NHTS trip rates may represent a stabilization or an overestimation of transit use by seniors when applied to future years.

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114 The derived transit market size is only an estimation; however, inferences may be gained by way of the magnitude of any resulting change that may assist in the strategic planning of future transit services for seniors by transportation providers. Table 4.24 presents estimates of the transit market share of seniors in the base and forecast years (see Appendix H for detailed calculations). Estimates indicate that the number of transit trips for seniors is set to double by the year 2030, when compared to those made in 2001. With this doubling of trans it trips by seniors, it can also be seen that the market share attributable to seniors will also increase, from 8 percent to 13 percent. Table 4.24 Senior Transit Market Share Assessment #1* Trip/Population Cohort 2001 2030 Total Trips 0 64yrs 366,272,055,294437,949,301,366 Total Trips 65yrs+ 40,990,429,91389,067,705,829 Transit Trips 0 64yrs 6,149,312,0167,352,695,523 Transit Trips 65yrs+ 503,068,6831,093,113,040 0 64yr population 244,319,167292,130,964 65yrs+ population 32,884,06871,453,471 Senior Transit % 7.56%12.94% see Appendix H for det ailed calculations 4.10 Transit Market Share Results – Market Assessment #2 In Market Assessment #2, senior transit trip making is broken down into urban and rural categories (see Appendix H for detailed calculations). In section 3.14.2 it was evident that 98 percent of transit trips took place in the urban environment. Indeed, of the 503 millions transit trips made by seniors in 2001, 99 percent were made in an urban area. In deriving an estimate of the transit market share in 2030 according to an urban/rural split the following is assumed:

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115 The urban/rural population split for persons ages 65 years and older in 2030 will be similar to that prevailing in year 2000. According to 2000 Decennial Census data, 26.8m persons 65 years and older (77%) lived in metropolitan areas and the balance, i.e., 23 percent lived outside metropolitan areas (in the rural areas) (He et al. 2005, p. 138). NHTS data estimated the urban/rural split of the senior population at similar proportions, 78 percent urban and 22 percent rural. According to the NHTS 2001 an Urban Area is defined as a, “built up area surrounding a central core (or central city), with a population density of at least 1,000 persons per square mile.” (U.S. Department of Transportation, Appendix E, 2004) A rural area is therefore taken to be an area with a density less than 1,000 persons per square mile. Research has shown that most older people do not move, (He & Schachter 2003, p. 2), and this fact has contributed to the phenomenon “aging in place.” The concept of aging in place is defined as “not having to move from one's present residence in order to secure necessary support services in response to changing need” (Seniorresource.com, 2006). Thus, it is assumed that in the majority of cases (and the preferred choice of), persons ages 35 years and older in 2000, if alive in 2030, will be in a similar residential setting (urban/suburban/rural) as they were in 2000. Frey (2003, p. 6), notes that, “roughly 70 percent of all 35 – 54 year olds in large metro areas lived in the suburbs.” Total number of trips and transit trips for the urban/rural population in 2030 is based on the daily trip and transit trip rates for the urban/rural population in 2001 (as derived from the NHTS 2001) multiplied by the population estimates for 2030.

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116 Transit trip rates by age cohort and gender for the senior population derived from the 2001 NHTS are applied to the year 2030. Thus, seniors in 2030 are assumed to display similar transit use behaviors as evident in the NHTS 2001. Tables 4.25 and 4.26 present estimates of the transit market share of seniors in the base and forecast years. In the year 2001, senior transit users residing in urban areas were responsible for 8 percent of the transit market (see Figure 4.8). In 2030, this proportion of transit market share according to the methodology developed in this research project is estimated to increase to 13 percent. However, given the dominance of transit trips undertaken in urban areas, the positive 2 percentage point change in market share for seniors in rural areas between 2001 and 2030 in Table 4.8 may be a plausible estimate, assuming rural transit service patterns do not change significantly in future decades. Table 4.25 Senior Transit Market Share Assessment #2 (Year 2001)* Trip/Population Cohort Urban Rural Total Total Trips 0 64yrs 289,645, 261,20176,626,794,127 366,272,055,328 Total Trips 65yrs+ 32,434,626, 4858,555,803,425 40,990,429,910 Transit Trips 0 64yrs 6,018, 647,645130,664,372 6,149,312,017 Transit Trips 65yrs+ 500, 341,6852,726,998 503,068,683 0 64yr population 190,95 0,30853,368,861 244,319,169 65yrs+ population 25,622, 4997,261,571 32,884,070 Senior Transit % 7.68%2.04% 7.56% see Appendix H for det ailed calculations Table 4.26 Senior Transit Market Share Assessment #2 (Year 2030)* Trip/Population Cohort Urban Rural Total Total Trips 0 64yrs 341,204, 210,13996,471,304,201 437,675,514,340 Total Trips 65yrs+ 69,646,848, 85219,363,389,264 89,010,238,116 Transit Trips 0 64yrs 7,090, 010,405164,503,324 7,254,513,730 Transit Trips 65yrs+ 1,074,38 3,3216,171,708 1,080,555,029 0 64yr population 224,94 0,84267,190,122 292,130,964 65yrs+ population 55,019, 17316,434,298 71,453,471 Senior Transit % 13.16%3.62% 12.96% see Appendix H for det ailed calculations

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117 Again, estimates indicate that the number of urban transit trips for seniors are set to double by the year 2030, when compared to t hose made in 2001. With this doubling of urban transit trips by seniors, it can also be seen that the market share attributable to seniors will also increase from 8 percent to 13 percent. 4.11 Transit Market Share Results – Market Assessment #3 Market Assessment #3 takes the urban seniors and categorizes them according to their driving status (see Appendix H for detailed calculations). In deriving transit use estimates for 2030 in this market assessment, estimates of former drivers will also be incorporated. At this juncture in the analysis of the NHTS 2001, of the 32 million trips made by seniors in urban areas, 29 million we re made by senior drivers and 3 million by senior non-drivers. However, the following is also assumed in deriving estimates for Market Assessment #3: Drivers are persons ages 15 years and above. Correspondingly, non-drivers are persons ages 0 to 14 years, non-licensed adults ages 15 years and above, and adult licensed but non-active drivers. Only seniors residing in an urban area, according to the NHTS 2001, are considered. Thus, in this market assessment, of the 26 million urban seniors, 20 millions are drivers (78%) and 6 millions (22%) non-drivers. Estimates of future drivers and non-dr ivers (year 2030) ages 15 to 64 years are determined by the average licensing proportions of this age cohort between the years 2000 and 2004. Analysis reveals that 87 percent of males and 85 percent of females (15 to 64 years) were, on average, licensed for this period. However, a licensing proportion of 85 percent for both males and females will be used in this market assessment analysis.

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118 Estimates of future drivers and non-drivers (year 2030) in senior age cohorts will follow the proportions as developed by Foley et al. (2002) in section 3.12.2 (and presented in Table 3.15). For example, in 2030, estimates indicated that 66 million of the 71 million seniors (65 years+) will be licensed. However, 6 million will be former drivers and 6 million never licensed; thus, a total of 12 million will represent the non-driving senior population. The total numbers of trips and transit trips for the urban senior population in 2030 are based on the daily trip and transit trip rates for population of senior drivers and nondrivers in 2001 (as derived from the NHTS 2001) multiplied by the population estimates for 2030. Transit trip rates by age cohort and gender for the senior population derived from the 2001 NHTS are applied to the year 2030. Thus, seniors in 2030 are assumed to display similar transit use behaviors as evident in the NHTS 2001. Tables 4.27 and 4.28 present estimates of the transit market share of seniors in the base and forecast years. In the year 2001, non-dr iving seniors residing in urban areas had a 13 percent transit market share, when compared to their driving counterparts with 4 percent. Estimates of licensure status for seniors (see section 3.8) have indicated that Table 4.27 Senior Transit Market Share Assessment #3 (Year 2001)* Trip/Population Cohort Active DriverNon-Driver Total Total Trips 0 64yrs 213,313, 223,56176,332,037,616 289,645,261,177 Total Trips 65yrs+ 29,216,362, 7813,218,263,711 32,434,626,492 Transit Trips 0 64yrs 2,935,34 3,7433,083,303,922 6,018,647,665 Transit Trips 65yrs+ 159, 513,500340,828,189 500,341,689 0 64yr population 127,11 3,55063,836,759 190,950,309 65yrs+ population 19,892, 9255,729,575 25,622,500 Senior Transit % 5.15%9.95% 7.68% see Appendix H for det ailed calculations

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119 Table 4.28 Senior Transit Market Share Assessment #3 (Year 2030)* Trip/Population Cohort Active DriverNon-Driver Total Total Trips 0 64yrs 242,216, 674,50496,380,904,381 338,597,578,885 Total Trips 65yrs+ 68,191,259, 2894,824,328,925 73,015,588,215 Transit Trips 0 64yrs 3,333,07 6,0663,893,144,082 7,226,220,147 Transit Trips 65yrs+ 372, 305,975510,917,513 883,223,488 0 64yr population 144,33 7,14480,603,699 224,940,842 65yrs+ population 46,430, 2708,588,903 55,019,173 Senior Transit % 10.05%11.60% 10.89% see Appendix H for det ailed calculations over 90 percent of the young old will have been licensed at some stage in their lives; these anticipated high licensure rates for seniors in the forthcoming decades may have contributed to the marginal estimated increase in transit market share for senior nondrivers in the year 2030 of 2 percent when com pared to active drivers of 4 percent (even when taking into account the addition of senior former drivers to the non-driving population), as presented in Table 4.28. It can also be noted that, in 2001, senior nondrivers were 22 percent of the senior urban population: this proportion in 2030 is estimated to decrease to 15 percent. 4.12 Transit Market Share Results – Market Assessment #4 Market Assessments #4 and #5 take the urban non-driving seniors and categorizes them by availability of drivers or vehicles in their households (see Appendix H for detailed calculations). The primary reason for focusing on seniors in this particular category is that the lack of a drivers’ license or a hous ehold vehicle is a strong predictor of transit use particularly in an urban environment (ICF Consulting 2006). One only has to look at the transit market share estimates for Market Assessments #4 and #5 to gauge the greater contribution to transit market share by non-driving seniors or those without household vehicles at their disposal.

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120 In Market assessment #4 which focuses on the availability of a driver in the household, for those non-driving seniors living alone, of course, there will not be another driver in the household. However for other non-driving seniors living in households of more than one person, other persons in the household holding driving status may be in a position to facilitate out-of-home mobility for these non-driving seniors. Market Assessment #4 attempts to ascertain the significance of non-driving seniors residing in households with no other driving adults and their propensity to use transit. In deriving transit use estimates for 2030 in this market assessment, estimates of former drivers will also be incorporated. At this juncture in the NHTS, of the 32 million trips made by seniors, 29 million were m ade by urban senior drivers and 3 million by urban senior non-drivers (see Table 4.27). However, the following is also assumed in deriving estimates for Market Assessment #4: Drivers are persons ages 15 years and above. Correspondingly non-drivers are persons ages 0 to 14 years, non-licensed adults ages 15 years, and adult licensed but non-active drivers. Only non-driving seniors in an urban area according to the NHTS 2001 are considered in Market Assessment #4. Thus, in this market assessment of the 5.7 million urban non-driving seniors (see Table 4.27), 2.3 million reside in households were there are zero drivers and 3.3 million reside in households where a driver is present. Table 4.29 presents this information. Table 4.29 Urban Non-Driver Respondent According to Household Driver Availability (Year 2001) Zero Drivers in Household Driver in Household Driver/Non-Driver Household Split P opulation Percent Population Percent 0 64yr population* 5,358,8088.4%58,477,956 91.6% 65yrs+ population* 2,348, 85941.0%3,380,717 59.0% *Urban non-drivers Source: NHTS 2001 Person File

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121 With each passing decade into the future, it is likely that there will be higher proportions of persons in adult age cohorts that will be licensed. Thus, estimates of the proportions of non-drivers residing in house holds with drivers or zero drivers will also change from the 2001. In 2001, according to the NHTS, approximately 33 percent of non-drivers (0 – 64 years) and 22 percent of senior non-drivers resided in driver or zero driver available households, as indicated in Table 4.29. With higher percentages of licensed persons in 2030, the predicted proportions of persons residing in driver and zero driver available households are contained in Table 4.30. Table 4.30 Urban Non-Drivers According to Household Driver Availability (Year 2030)** Zero Drivers in Household Driver in Household Driver/Non-Driver Household Split P opulation Percent Population Percent 0 64yr population* 4,030,1855.0%76,573,514 95.0% 65yrs+ population* 639, 91415.0%3,626,177 85.0% *Urban non-drivers ** See Table H.XX Annexure H The total numbers of trips and transit trips for the urban non-driving senior population in 2030 are based on the daily trip and transit trip rates for the population of urban senior non-drivers in 2001 (as derived fr om the NHTS 2001) multiplied by the population estimates for 2030. Transit trip rates by age cohort and gender for the senior non-driving population derived from the 2001 NHTS are applied to the year 2030. Thus, seniors in 2030 are assumed to display similar transit use behaviors as evident in the NHTS 2001. Tables 4.31 and 4.32 present estimates of the transit market share of seniors in the base and forecast years. In the year 2001, nondriving seniors residing in zero driver households had a 15 percent transit market share, when compared to non-driving

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122 counterparts residing in homes where a driver is present with 7 percent. Estimates for 2030 indicate that senior non-drivers in zero driver households will decrease by 3 percentage points to account for only 12 percent of the transit market share. This is a plausible result given the higher proportions of persons licensed to drive in 2030 and the increasing likelihood that a higher proportion of seniors will be living in a household with a driver available. Table 4.31 Senior Transit Market Share Assessment #4 (Year 2001)* Trip/Population Cohort Zero Driver in Household Driver in Household Total Total Trips 0 64yrs 5,392,524, 66170,939,512,962 76,332,037,623 Total Trips 65yrs+ 1,343,355, 8661,874,907,841 3,218,263,707 Transit Trips 0 64yrs 1,433,16 5,5761,650,138,340 3,083,303,916 Transit Trips 65yrs+ 257, 148,54283,679,644 340,828,186 0 64yr population 5,358, 80858,477,956 63,836,764 65yrs+ population 2,348, 8593,380,717 5,729,576 Senior Transit % 15.21%4.83% 9.95% see Appendix H for det ailed calculations Table 4.32 Senior Transit Market Share Assessment #4 (Year 2030)* Trip/Population Cohort Zero Driver in Household Driver in Household Total Total Trips 0 64yrs 4,055,542, 14092,891,204,607 96,946,746,747 Total Trips 65yrs 736,822,839 4,048,813,055 4,785,635,894 Transit Trips 0 64yrs 1,077,83 7,1452,160,761,073 3,238,598,219 Transit Trips 65yrs+ 141, 044,472180,703,941 321,748,412 0 64yr population 4,030, 18576,573,514 80,603,699 65yrs+ population 1,288, 3357,300,567 8,588,903 Senior Transit % 11.57%7.72% 9.04% *see Appendix H for deta iled calculations 4.13 Transit Market Share Results – Market Assessment #5 Market Assessment #5 is similar to Market Assessment #4 but looks at the availability of vehicles in households of senior non-driving respondents (see Appendix H for detailed calculations). Market Assessment #5 attempts to ascertain the significance of nondriving seniors residing in households with or without vehicles and their propensity to use transit. In deriving transit use estimates for 2030 in this market assessment,

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123 estimates of former drivers will also be incorporated. However, the following is also assumed in deriving estimates for Market Assessment #5: Drivers are persons ages 15 years and above. Correspondingly, non-drivers are persons ages 0 to 14 years, non-licensed adults ages 15 years, and adult licensed but non-active drivers. Only non-driving seniors in an urban area according to the NHTS 2001 are considered in Market Assessment #4. Thus, in this market assessment, of the 5.7 millions urban non-driving seniors, 2.3 million reside in households where there are zero vehicles and 3.3 million reside in households where at least one vehicle is available. Table 4.33 presents this information. Table 4.33 Urban Non-Driver Respondent According to Household Vehicle Availability (Year 2001) Zero Vehicles in Household Vehicle in Household Driver/Non-Driver Household Split Population Percent Population Percent 0 64yr population* 7, 436,02911.6%56,400,734 88.4% 65yrs+ population* 2,376, 60941.5%3,352,962 58.5% *Urban non-drivers Source: NHTS 2001 Person File With each passing decade into the future, it is likely that there will be higher proportions of persons in adult age cohorts that will be licensed. Thus, estimates of the proportions of non-drivers residing in house holds with drivers or zero drivers will also change from the 2001. Accepting that there will always be licensed persons who do not have access to a vehicle in their household, 2030 estimates for the availability of at least one vehicle in a household will be 5 percentage points less than those shown in Table 4.30. Estimated proportions are presented in Table 4.34.

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124 Table 4.34 Urban Non-Drivers According to Household Vehicle Availability (Year 2030)** Zero Drivers in Household Driver in Household Driver/Non-Driver Household Split P opulation Percent Population Percent 0 64yr population* 8, 060,37010.0%72,543,329 90.0% 65yrs+ population* 853, 21820.0%3,412,873 80.0% *Urban non-drivers ** See Table H.XX Annexure H The total numbers of trips and transit trips for the urban non-driving senior population in 2030 are based on the daily trip and transit trip rates for the population of urban senior non-drivers in 2001 (as derived fr om the NHTS 2001) multiplied by the population estimates for 2030. Transit trip rates by age cohort and gender for the senior non-driving population derived from the 2001 NHTS are applied to the year 2030. Thus, seniors in 2030, are assumed to display similar transit use behaviors as evident in the NHTS 2001. Tables 4.35 and 4.36 present estimates of the transit market share of seniors in the base and forecast years. In the year 2001, nondriving seniors residing in zero vehicle households accounted for 14 percent of the transit market share, when compared to non-driving counterparts residing in homes wher e a vehicle was present, with 4 percent. Estimates for 2030, indicate that senior non-drivers in zero driver households will decrease by 4 percentage points to only account for 10 percent of the transit market share. Table 4.35 Senior Transit Market Share Assessment #5 (Year 2001)* Trip/Population Cohort Zero Driver in HH Driver in HH Total Total Trips 0 64yrs 7,658,773, 57768,673,264,049 76,332,037,626 Total Trips 65yrs+ 1,353,076, 7561,865,186,948 3,218,263,704 Transit Trips 0 64yrs 1,801,48 5,5761,281,818,344 3,083,303,920 Transit Trips 65yrs+ 284, 969,78955,858,397 340,828,186 0 64yr population 7,436, 02656,400,733 63,836,759 65yrs+ population 2,376, 6093,352,962 5,729,571 Senior Transit % 13.66%4.18% 9.95% *see Appendix H for deta iled calculations

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125 Table 4.36 Senior Transit Market Share Assessment #5 (Year 2030)* Trip/Population Cohort Zero Driver in HHDriver in HH Total Total Trips 0 64yrs 6,297,295, 30521,657,271,087 27,954,566,392 Total Trips 65yrs 2,028,327,21 82,789,063,655 4,817,390,873 Transit Trips 0 64yrs 1,877,93 9,9341,096,558,934 2,974,498,867 Transit Trips 65yrs+ 427, 183,43883,734,427 510,917,865 0 64yr population 9,389, 12371,214,575 80,603,699 65yrs+ population 3,562, 6515,026,252 8,588,903 Senior Transit % 18.53%7.09% 14.66% *see Appendix H for deta iled calculations Three of the five market analyses indicated an increase in transit market share attributable to seniors between the base year 2000/2001 and the forecast year 2030. Market analyses 4 and 5 indicated a decrease in transit market share. In these latter analyses, higher licensure proportions of seniors, coupled with higher levels of active driving and/or vehicle access, eroded predict ed transit market size, even with the doubling in the absolute numbers of seniors during this period. 4.14 Market Share Sensitivity Analyses Three sensitivity tests were performed on Market Analysis #4 to illustrate potential changes in transit market share when licensing or cessation rates are equal between genders. For example, if driving cessation rates between males and females equalize coupled with longer driving histories, there is likely to be further change in the transit market share due to seniors as indicated in Table 4.32. The three tests performed were: Driving licensure rates equal between genders (female licensure rates equal that of males); Cessation rates (according to Foley et al. 2002) equal between genders (female cessation rates equal that of males); and Driving licensure and cessation rates equal between genders.

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126 Results of these sensitivity analyses are contained in Tables 4.37 to 4.39 (and Appendix J presents calculations in the derivations of these tests). The assumptions with respect to Market Analysis #4 (see section 4.12) also apply in these tests. Table 4.37 Senior Transit Market Share Market Assessment #4 Gender Licensing Equal (Year 2030)* Trip/Population Cohort Zero Driver in Household Driver in Household Total Total Trips 0 64yrs 4,055,542, 14092,891,204,607 96,946,746,747 Total Trips 65yrs 631,488,778 3,470,006,456 4,101,495,234 Transit Trips 0 64yrs 1,077,83 7,1452,160,761,073 3,238,598,219 Transit Trips 65yrs+ 120, 881,162154,871,028 275,752,190 0 64yr population 4,030, 18576,573,514 80,603,699 65yrs+ population** 1,104, 1596,256,899 7,361,058 Senior Transit % 10.08%6.69% 7.85% *Compare with Table 4.31 **Female licensing rates equal to males s ee Appendix J for detailed calculations Table 4.38 Senior Transit Market Share Market Assessment #4 Gender Cessation Rates Equal (Year 2030)* Trip/Population Cohort Zero Driver in Household Driver in Household Total Total Trips 0 64yrs 4,055,542, 14092,891,204,607 96,946,746,747 Total Trips 65yrs 636,130,839 3,495,514,403 4,131,645,242 Transit Trips 0 64yrs 1,077,83 7,1452,160,761,073 3,238,598,219 Transit Trips 65yrs+ 121, 769,757156,009,482 277,779,239 0 64yr population 4,030, 18576,573,514 80,603,699 65yrs+ population** 1,112, 2756,302,894 7,415,169 Senior Transit % 10.15%6.73% 7.90% *Compare with Table 4.31 **Female cessation rates equal to males s ee Appendix J for detailed calculations Table 4.39 Senior Transit Market Share Market Assessment #4 Gender Licensing and Cessation Rates Equal (Year 2030)* Trip/Population Cohort Zero Driver in Household Driver in Household Total Total Trips 0 64yrs 4,055,542, 14092,891,204,607 96,946,746,747 Total Trips 65yrs 521,032,477 2,863,053,349 3,384,085,826 Transit Trips 0 64yrs 1,077,83 7,1452,160,761,073 3,238,598,219 Transit Trips 65yrs+ 99, 737,341127,781,899 227,519,240 0 64yr population 4,030, 18576,573,514 80,603,699 65yrs+ population** 911, 0265,162,479 6,073,505 Senior Transit % 8.47%5.58% 6.56% *Compare with Table 4. 31 **Female licensing and cessation rates equal to ma les see Appendix J for detailed calculations

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127 4.15 Market Assessment Summary The five different market assessments gave an indication as to a probable change in transit market share attributable to senior trip makers in 2030 (assuming 2001 trip rates). Table 4.40 summarizes the market assessment results, of seniors residing in urban areas who may have a greater propensity to use transit i.e., those who are nonor former drivers and live in households with ze ro drivers or vehicles. Table 4.40 also indicates the percentage point change in transit market share attributable to seniors in the categories described, between the base year 2000/2001 and the forecast year 2030. Table 4.40 Overall Market Assessment Results Market Assessment Base Year 2000/2001 Forecast Year 2030 Percentage Point Change Market Assessment #1: All Seniors 7.56%12.94% 5.38 Market Assessment #2: Senior Transit Market – Urban 7.56%12.96% 5.40 Market Assessment #3: Senior Transit Market Urban Non-Driver Status 7.68%10.89% 3.21 Market Assessment #4: Senior Transit Market Urban Non-Driver & Zero Driver Availability 9.95%9.04% -0.91 Market Assessment #5: Senior Transit Market Urban Non-Driver & Zero Vehicle Availability 9.95%8.17% -1.78 The three sensitivity tests performed repr esenting hypothetical scenarios provided additional insight into potential transit market changes. Equalization of cessation or licensure rates (or both) between males and females may result in negative changes in future transit market shares attributable to seniors. 4.16 Seniors Perceptions and Experiences with Transit To complement the the market analyses with respect to the potential future use of transit by seniors, qualitative methodology wa s employed to elicit views of seniors on

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128 their perceptions and experiences of transit. The subsequent sections present the results of the focus group discussions. 4.16.1 Focus Group Methodology Five focus group discussions, including one pilot session, were conducted with seniors 55 years and older in Hillsborough County at senior centers located in urban, semi-urban and semi-rural areas of the county. This strategy increased the potential that a diversity of individuals would participate, reflecting the senior population makeup of the county. The focus groups were conducted in late January and early February 2006. Participants were recruited with the help of the County Aging Services Department through advertising the focus group discussions at senior centers under their jurisdiction. For more details on the format of and overall findings from the focus group discussions sessions, the reader is referred to Polzin & Page (2006). Each focus group discussion lasted no more than 90 minutes. Two researchers from the University of South Florida (USF) Center for Urban Transportation Research (CUTR) were present at each session, a moderator and an observer. The format at each focus group session included a welcom e and introduction with an explanation of focus group participant rights, discussion, and questionnaire completion, followed by closure and thanks. A questionnaire was designed to provide socio-demographic information as well as further probe issues raised in the discussions in order to undertake quantitative analysis. Two types of questionnaires were given, one for former drivers (i.e., those who had permanently stopped driving) and another for current drivers (i.e., those who had reduced their driving exposure). Each of these questionnaires is presented in Appendix K. Each focus group discussion was digitally recorded and transcribed afterward. The discussion transcrip ts as well as results from participant

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129 surveys were combined to create a holistic perspective of senior issues and concerns about and their potential use of public transportation as one of several transportation alternatives during and after the process of driving retirement. 4.16.2 Factors Initiating Use of Public Transportation Discussion of factors that would influence focus group participants to include public transportation as a transportation option elicited a variety of responses. Responses are summarized as follows. Cost Many focus group participants indicated that if there were a cost for use of public transportation it would have to be free or affordable. There was some debate about any cost charged being determined by one’s income, but it had to be a fair price and have minimal impact on one’s pocketbook. Another cost that may influence the use of public transportation is the cost of a parking ticket. One focus group participant stated that he used the bus to go downtown on personal business to avoid getting a ticket. Accessibility Being able to access services closer to one’s home and delivery and pickup closer to the destination were cited as factors that could induce use of public transportation. Door-to-door service was the preferred option. If one had to drive to access public transportation, this would detract from using public transportation altogether for the trip in question. Continuing with the accessibility theme, a focus group participant noted that, not only is the distance to the access point, (bus stop) important, but the environment at that point also contributed to her current non-use of local transit services. She stated that, “the stops are so far away, you can’t get to those. I am a

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130 mile away from the first, from the nearest st op which is about right at a bar where the drunks are hanging out.” The non-availabili ty of public transportation near one’s home was another factor that discourages considering this mode as a viable transportation alternative for the majority of focus group participants. Destinations Served Public transportation services serving the destination of interest was another factor that may induce use of the service by the focus group participants. Having access to public events, theme parks (e.g., Busch Gardens), or areas of natural beauty was another issue raised by some focus group par ticipants. They wanted to visit these places but were limited by physical ability, cost of transportation (having to take a taxi), and lack of information about public transportation access to these places. Level of Family Involvement The strength of familial relationships has an impact on the use of public transportation. The transportation needs of several focus group participants were met entirely by family members or friends. On the other hand, striving to lessen the inconvenience to family members/friends while at the same time maintaining self dignity and independence when asking for rides was another factor influencing some focus group members to consider and even use transit. Past Experiences with Public Transportation Focus group participants who had lived part of their lives in a transit rich environment, e.g., New York City, had allowed these past experiences to determine their perception of public transportation in their current location. In all cases, comparing transportation services in a city such as New York to those provided in their current location would not be a fair comparison. These past experiences relegated fixed route services provided in their current location to be described by

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131 such negative terms as “really, really bad,” “no good” or “terrible.” The extent of the dislike of local public transportation services could be seen in the faces of several focus group participants as they discussed their use or non-use of public transportation. Travel Time and Service Frequency Several focus group participants noted the long travel times (the actual line haul trip plus the waiting times at either end of the journey) and low frequency of buses as a deterrent to using public transportation. A frequency of one bus every 15 – 20 minutes was cited by a focus group participant as having a positive impact on their potential use of public transportation. Focus group participants who had used paratransit also were concerned with the travel time window that was either too long (i.e., waiting for service to arrive) or too short (i.e., not enough time to get to the service when at your door). Figure 4.14 presents results to the question, “What one factor, if changed, would make public transit an option for you to use today?” It is evident that cost (i.e., free or low cost public transit services) and accessibility (i.e., closer to my home and easier to get on or off) are the two highest ranked factors to the 36 focus group participants who answered this question (eight declined). Four participants indicated that, despite any improvements of public tr ansportation services, they still would not use it. The perceived lack of personal safety has been cited as a factor seniors mention as a reason to avoid using transit. None of the focus group participants indicated that crime on local public transit se rvices detracted from them using it or that improving the personal safety envir onment on local transportation might induce them to use it.

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132 4.16.3 Concerns about Using Public Transit Focus group participants were asked to indicate their primary concern about using public transit. Responses (37) are presented in Figure 4.15. Service area (i.e., public transit does not go where I want to go) was the foremost factor giving rise to concern, followed by transit information (i.e., lack of information about public transit). These two factors are interrelated as the lack of knowledge about the public transit options in an area may be due to a lack of information about public transit in general. More information and training about using public transit may increase consideration of its use by seniors. Transit Information For many focus group participants, the lack of information about transit services served as a factor in its non-use. Indeed, as to the limited knowledge of the local transit services, consensus reached am ong focus group participants indicated that this was “partly because we don’t have to use it yet.” This response indicates that Figure 4.14 Factors Enhancing Potential Use of Public Transportation Free or Low Cost 31% More Destinations 11% Faster Service 8% Accessibility 25% Higher Frequency 6% Information & Training 8% Nothing 11%

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133 interest in transit services is partly due to having to use it, if one does not need to use transit, there is no need to why find out about what benefits it can offer. The non-interest in the local transit services was further confirmed by a participant who stated that, “I don’t bother finding out [about transit services] where it [the transit service] does go here, because we know it doesn’t go our way, so we didn’t bother with the other.” Service Area For focus group members situated in semi-rural areas of Hillsborough County, transit services and coverage were limited and often associated with long travel times to complete a round trip. An experience shared by a focus group member related her frustration at the long travel times and the circuitous routing of the bus while traveling only a few miles to a large shopping mall from her semi-rural home. It was pointed out to focus group members that fixed route transit service, adhere to a preService Area 38% Travel Time 11% Accessiblity 16% Frequency 11% Transit Information 24% Figure 4.15 Factors Influencing Concerns About Using Public Transit

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134 designated route and only served stops along that route. However, focus group participants who lived a few blocks away from a particular stop perceived that they were not directly served (i.e., on their street ) by the local transit service, despite the fact that the nearest bus stop was only few blocks from where they lived. Thus, the use of any local transit services was dismissed. Adjusting the local fixed route transit service to meet all rider demands would possi bly result in a circuitous routing with commensurate lengthening in travel times, decreasing its attractiveness to potential riders. Service Accessibility Some focus group participants realized that transit service was available in their area, but accessing the service was a chall enge. For many it was too far away to walk, requiring transportation to get to the bus stop. Furthermore, if transportation were available to take them to the bus stop, why not use the transportation service for the whole trip instead of transferring to transit? This latter reasoning was particularly evident in the case of seniors using park and ride facilities. Some focus group participants who used park and ride facili ties preferred smaller venues close to where they lived, rather than using the regular (i.e., large) facilities situated at some distance from their home. Given the propensity of seniors to travel during the offpeak periods, arriving during such a time may involve additional time being spent finding a parking space. This factor unique to park and ride facilities may have the potential to lessen future transit use for seniors who may still be driving but would consider using transit if the conditions were favorable.

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135 4.16.4 Viability of Using Public Transit in the Future Focus group participants were asked “Do you think that public transit is a viable option for you to use today?” Overall results pres ented in Table 2.3 (n = 42) indicated a 50:50 split, with 21 focus group participants indicating “yes” and 21 “no.” Breaking down these results by driver type (i.e., former and current drivers), the responses are shown in Table 4.41. Table 4.41 Viability of Future Consideration of Public Transportation as a Transportation Alternative Driving Status Yes No Total Current Drivers 11 (26%) 15 (36%) 26 Former Drivers 10 (24%) 6 (14%) 16 Total 21 (50%) 21 (50%) 42 The results contained in Table 4.41 indicate that, among current drivers, 15 (60%) of the 26 focus group participants who responded felt that public transportation is not a viable transportation option for them. This result may have been influenced by their nonor limited use, non-availability or negative perceptions held about public transportation. A similar percentage (62%) of former drivers (10 out of 16) responded positively. Possible factors contributing to this result may have been that this group had investigated transportation options as former drivers and having had recent experience with using public transportation, coupled with a change of attitude towards this mode arising from their experience. It can be noted that interpretation of the viability of public transportation by focus group participants in meeting their transportation needs may not, in reality, result in the actual use of this mode. Focus group participants may require a variety of interventions to be in place in order for them to use public transportation, some of which may be economically unviable for a transportation pr ovider to implement for the market being

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136 served. However, despite the inconclusive result, focus group participants did indicate a variety of factors that would influence them to consider transit as a mobility option.

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137 CHAPTER 5 – CONCLUSIONS AND RECOMMENDATIONS 5.1 Introduction This concluding chapter provides an overview of the research undertaken and explores the potential implications of the research. Through analysis of a variety of market assessments, it has become clearer how the number of active and former drivers affect the transit market in the future. However, transit agencies cannot be assured that a burgeoning transit market can be guaranteed with the maturing of the baby boom generation, given the dynamic nature of senior travel behavior and preferences, especially post driving cessation. Lessons that can be learned from this research are discussed in the following sections. 5.2 Transit Market Size In 2001 seniors accounted for 33 million persons and comprised 8 percent of transit market share, according to the NHTS 2001 (Table 4.24). Estimated results from this research indicate that this market may increase to 71 million persons responsible for 13 percent of transit market share in 2030 (Table 4.24; assumes trip rates of seniors in 2030 are similar to those in 2001). Despite this increase, it is evident that a doubling of the senior population does not lead to a doubling in the size of the associated market share in 2030. Indeed, transit agencies should take note that, with the increasing proportions of licensed seniors in forthcoming decades, and seniors living in better health and possibly at higher levels of financial wealth, there will be a greater likelihood that the actual use levels of transit (transit mode share) by seniors may actually decline.

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138 Indeed, the increase in licensure rates will offset the potential growth in the non-driving senior transit market. Transit will continue to provide service to more driving seniors (choice transit users); however, this is a more challenging market for tranit. To test the stability of the estimates derived, the sensitivity tests (contained in Tables 4.37 to 4.39) also confirmed the negative relationship between increasing licensure/cessation rates and transit market share. The resulting shrinkage in transit mode share in future decades is confirmed in another study that modeled the aging population and transit ridership. This study found that “an increased older population depresses regular transit ridership (especially for buses) while increasing paratransit use” (ICF Consulting 2006, p. 39). However, determining the magnitude of the senior transit market is complicated by inadequate es timates of the numbers of former drivers and the accessibility to and quality of transportation alternatives available to this group post-cessation. 5.3 Driving Transition and Subsequent Transportation Options Despite the increases in active life expectancy, for many seniors it is inevitable that, at some stage in their driving career, there will come a point where driving will be a challenge and the option is taken to retire from driving. What options will be available in 2030 for seniors at this juncture in their driving lives? Furthermore, what proportions of the seniors who have retired from driving will transition to the various transportation alternatives. Figure 5.1 presents eight different choices in how seniors in 2030 may facilitate their out-of-home mobility in 2030. Each transportation alternative offers different levels of service quality, and the availability of transit does not make the transportation choice of the senior any easier nor ease the transition from an active driver to former driver.

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139 The presumption that seniors after driving cessation place a high value on transit availability and thus become transit patrons is incorrect. The availability of transit at a level not meeting a senior’s transportation need may prolong the driving career of a senior who wants to avoid using a service that does not meet their needs. Increasing numbers of post-driving seniors will not translate into increasing numbers of transit users, if the current status of transit services perpetuates into the future. The first transportation choice for many seniors post-driving cessation is riding with family or friends. However, as discussed in Chapter 2, falling fertility rates in recent decades may result in a situation where, for some seniors in 2030, there may not be an available adult child to facilitate their transportation needs. For some seniors, will nonprofit transportation service providers step in to meet the challenge, and will this option be dependent on group membership or some other predetermined qualification, that, if Figure 5.1 Post Cessation Transportation Options Senior Former Driver Riding with nonprofits, e.g., church groups or ITN America 2 Ride with for-profit, mobility companies e.g., taxis 3 Transit services fixed route/ paratransit 4 Walk/Cycle 6 Future transportation modes..? 5 Ride with family/friends (as a passenger) 1 Cease out-of home mobility 8 Continue driving 7

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140 not met may disqualify seniors from using pr ovided transportation services? Will there be a time in future decades where transit could be perceived on a similar service quality level as riding with friends/family or a non-profit transportation organization? With the introduction of new transportation modes in the future, the relative preference of options depicted in Figure 5.1 may be upset again, and one does not know how transit will be ranked in the new order of transportation choices. 5.4 Migration and Seniors The extent of migration preand post-retirement will impact the magnitude of transit market estimates. For many seniors who plan their retirement location during their middle age years, the issue of the prevailing transportation environment at the new location is not explicitly considered as a pull-factor. Indeed, it may be taken for granted or overlooked by the retiree. It is evident that, for the majority of factors mentioned by seniors precipitating migration, accessibility to the POV enables the benefits sought from such a migration to be realized. After the onset of driving cessation, some seniors may contemplate another move to a location that offers a range of transportation alternatives in addition to the POV. However, the extent of transit availability is one of several competing factors that may influence relocation as observed from focus group discussions. In this study, focus group participants were asked whether they would relocate so as to be near adequate transit services or closer to an adult child who could meet their transportation needs. For the majority of participants relocating to be closer to an adult child or to an area with adequate transit services, if such an area were situated in the Northeast or Midwest, relocation was not an option. Responses ranged from these regions being “too cold,” which would impac t expenditure on heating bills (and on a fixed

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141 income, this could prove a challenge to meet every month) and the “convenience of living in Florida” in terms of affordability. For other participants, they would only consider such a move after the death of a spouse/partner. The prospect of having to relocate placed a number of focus group participants in a quandary, on whether to stay in an area with limited transportation options or move to an area better served by transit. In numerous instances in locations like Florida, one can observe adult children relocating to be nearer to aging parents. Depending on the availability of adult children, close friends, or the desire to relocate, the desire to stay put was so strong that one focus group participant indicated that she would consider moving into an assisted living community in the surrounding area rather than relocate to the Northeast or Midwest. Many assisted living communities provide transportation, which meet the transportation needs of their residents. However, seniors who consider a move to such a community in all likelihood would be lost to the fixed-route transit market. For some focus group participants, it was preferable to remain in a warmer climate with limited mobility rather than relocate to an environment with many mobility choices but limited access due to inclement weather. 5.5 Senior Conducive Transportation Environments Extension of the driving cessation process through continued self-initiated restrictions on driving behavior may prolong the driving experi ence of the senior to the detriment of using alternative transportation modes. Another aspect of the driving cessation process, is induced migration in order to continue dr iving in a conducive envir onment. Seniors in the driving cessation process may perceive t hat relocating to the exurbs (the extreme edges of the urban form) in preference to the central city with its associated transit services may offer relief from heavily congested suburban/urban traffic environments.

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142 The exurbs may offer a traffic environment that enables continued POV operation for the senior. However, the exurbs are even less transit friendly than the suburbs. Adequately serving the exurbs has been a chal lenge for many transit agencies, with the associated low population densities and greater distances between origins and destinations. However, when seniors in the exurbs do retire from driving, they may find themselves in an acute situation, as they may be further from family/friends (perhaps located in the suburbs necessitating longer trips to meet the travel needs) or be outside the service area of local transportation providers. In such a situation, meeting daily transportation needs may become prohibitively expensive, both in terms of cost and physical energy required, such that seniors may quickly find themselves isolated and disadvantaged. It is accepted that relocating to the exurbs may be partly due to affordability of homes in these areas. This may create an additional challenge for the senior who has the opportunity to relocate. Seniors may relocate to transit rich areas (downtowns) but may be challenge by housing affordability, or they may relocate to peripheral areas with affordable housing but limited (or non-existent) transit services. However, as noted above, once driving retirement begins, trans portation for seniors in the exurbs may become very expensive. Greater distances to travel will undoubtedly cost more in fares and travel time and with a possible inconveni ence to friends/family who have to provide the trips. 5.6 Working Seniors Recent reports (AARP 2005) have indicated the increasing numbers of seniors working post-retirement. Reasons for this development are seniors like what they do and want to keep doing it for as long as they can maintaining the value of savings and pensions, held

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143 and ensuring adequate social security and/or health insurance benefits post-retirement. The increasing numbers of seniors working past retirement age may offer a unique opportunity to transit agencies. Agencies may be able to rise to the challenge of meeting this need through the provision of ta ilored transit services as well as enhancing the role of transit as a transportation alternative. However, any potential expansion in the transit market arising from working seniors is dependent on the numbers of seniors who make the work commute trip versus telecommuting. 5.7 Meeting Transportation Needs Through Public Versus Private Provision Many seniors in the driving cessation proce ss would prefer to make the decision to stop driving themselves. However, family involvement can and does play a role in determining when the senior should stop driving. Future decades will bring an increase in the dependency ratio (discussed in Chapter 2), which, in turn, may result in seniors having to look outside their immediate family to meet their transportation needs. In both of these cases, one pressing question is the extent to which non-driver transportation needs will be met through public versus private initiatives? If there is family involvement in the driving cessation process, for this involvement to be complete, it may be preferable that the family also take the responsibility to meet the transportation needs of the former driver during and after the transition period. For some families, this may not create a challenge. However, will family members be able to meet all the transportation demands of the senior? For some seniors, the perception of being an inconveni ence to family members (through asking for a ride) may take a greater toll on their psyche than in a situation where an alternative transportation provider, e.g., taxi driver, is used. To reduce family involvement postcessation, will the senior relocate to a transit rich area to depend on public service

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144 provision of transportation? Seniors of tomorrow may feel that, since they have contributed over many years (by way of taxes) for their retirement, part of the retirement phase of their lives is access to adequate transportation services. Now that they have reached retirement, many feel “entitled” to public transportation services. 5.8 Implications for Senior Mobility Providers With the predicted doubling of the senior population in 2030 based on 2000 projections, what implications will this have fo r senior mobility providers? 5.8.1 Financial In many jurisdictions, seniors travel at reduce fares, often subsidized through taxes and other local authority revenue streams. With 8 percent of the 2001 transit market share attributable to seniors predicted to rise to 13 percent in 2030, transit providers will be challenged to accommodate a possibly increased proportion of reduced fare paying passengers while at the same time manage cost and service levels to maintain operational efficiency. The importance of the cost to use transit was confirmed in Figure 4.14, where free or low cost/fare was the most important factor influencing transit use by the focus group participants. The financial implications not only influence getting seniors to use transit but how they travel when they do use it. Seniors w ill expect to be able to access/exit a vehicle close to their home and will expect entering into/alighting from a vehicle to be relatively easy and safe. When riding the vehicle, seniors will also expect that a seat is available and, if assistance is required, e.g. lifting shopping bags, it is given by trained staff personnel. To meet any or all of these requi rements, there will be a cost attached. If transit providers do not meet the minimum stan dards of senior expectations with respect to transit use, seniors may chose alternatives other than ransit.

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145 Working seniors may imply wealthier seniors who are in a greater position to finance the travel choices they make. Many transit agencies subsidize senior fares, reducing the potential of farebox revenue meeting operating costs. However, wealthier seniors in the future may be able to contribute to transit travel through payment of a minimal charge. For those seniors who need to ride for free, subsidized fares can still be provided based on need rather than age. A positive implication of seniors directly contributing to transit services is that such contributions could be used for service enhancements, which, in turn, may attract more seniors to use transit services. 5.8.2 Operations The travel behavior profile of seniors presented in section 3.18 indicated that the majority of seniors travel during the offpeak periods (primarily between 11am and 3pm). This is a period where, for some transit operators, service frequency is scaled down from peak periods. Greater numbers of seniors traveling during off-peak periods may necessitate a revision of service frequencies during this time to meet senior demand. The prospect of a “transfer” on a trip often discourages seniors from using transit services; however, the increased numbers of seniors may create a market where direct routes during off-peak periods may be resumed. Maintaining social activity into the evening hours is another aspect of senior lifestyle that is important. Similarly, there may well be a need for transit properties to revisit evening schedules in order to stimulate and maintain demand. One benefit arising from in creased off-peak operations is an increase in vehicle and bus operator utilization efficiency. Seniors may also influence the schedule speed of vehicles if they impact the stop dwell time by needing extra time to board the v ehicle, pay fares or take a seat to avoid

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146 the risk of falling when the vehicle accelerates. This could have a cumulative impact on service cost and speed in locations with high concentrations of senior travelers. 5.8.3 Infrastructure As indicated above, seniors have and will cont inue to have expectations of travel by transit (and former drivers may expect tr ansit services to be operated “on-demand” as their POV). However, to capitalize on t hose seniors that may be contemplating transit use and retain the seniors who are already using transit, changes in transit infrastructure will have to keep pace with these expectations. Innovations such as low floor buses, (enabling easy entry/exit from the vehicle) ac cessible and safe bus shelters (protecting persons from the elements), speaking buses (bus location and route information are made audible) and obtaining information about services is simple, accurate and clear. These are but a few of infrastructure innovations, some of which incorporate ITS, that should be considered by transit agencies to make using transit by seniors (and everybody) easy. Nevertheless, such innovations must be well promoted directly to seniors in order to remedy any negative perceptions that seniors may have acquired over the years about transit use. 5.9 The Next Steps The estimates produced in this study paint one of several scenarios that may occur in the year 2030. Indeed, this research on transit use viability among older drivers after losing driving privileges resulted in: Licensed seniors (though not all active drivers) in the forecast year is estimated to be 92 percent of the total senior population (Table 4.4). Licensing proportion differences between genders will decrease in future years.

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147 An estimated 16 percent of the senior population may be classified as never-driven or former drivers in the year 2030 (Table 4.4), although driving cessation rates differ by methodological approach used as well as by gender. Driving cessation does not result in an automatic transition to transit. For many seniors (never licensed and former driver), ridesharing is the preferred choice for outof-home mobility. For transit to become a viable option for seniors (at least for consideration), services need to be free or low cost, accessible and serve a variety of destinations (Figure 4.14). How can the observed results from this res earch have practical applicability? A number of initiatives are presented as follows. Policy development Measures will need to be in place to accommodate the growing number of seniors who will have a diverse array of transportation needs to be satisfied in forthcoming decades. Seniors in 2030, the current baby boom generation of today, will have higher expectations of transit services, if such services are to be seen as a viable transportation alternative. Policy initiatives that can be put in place to enable a realization of this can include: o rewarding transit operators (through financial incentives) who provide services where seniors contribute in excess of a predetermined percentage (10 percent perhaps) of the total transit ridership o rewarding seniors who make a certain percentage of weekly trips by fixed route transit (discount shopping vouchers, free transit trip tickets)

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148 o enabling legislation that would permit a greater involvement of transit agencies in rideshare programs; in this case, transit agencies would not only own/manage buses but also car fleets. o enhancing lifelong learning standards that could encourage computer literacy of seniors through the provision of computer and internet access in each and every home to aid in getting transit information. Operational planning With respect to operational planning by transit agencies, a proactive approach will need to be maintained in order to meet the transportation demands of seniors in future decades. There will have to be a r ebalancing of transit service provision, which currently is focused on servicing the AM and PM peak periods to improve service provision in the off-peak periods. Indeed, policy will have to be developed that will encourage a mindset change surrounding the provision of premium transit services to full fare passengers (commuters) to accepting that all passengers of whatever fare class represent a market that can be nurtured, developed and maximized for operational benefit. 5.10 Study Limitations While undertaking this research a number of study limitations were identified. The use of empirical relationships to derive cessation rates for future senior populations was biased in favor of male drivers (males over 65 are licensed and drive to a much greater degree than their female counterparts), and incorporated wide differences in gender licensing rates. This will not be the case in future decades, as there will not only be a greater number of seniors but more senior females in particular will be licensed at levels never witnessed before in U.S. driver licensi ng history. Until this point is reached,

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149 cessation methodologies used to forecast senior former drivers may tend to overestimate the actual numbers of seniors that may be in the process of ceasing to drive. The focus group participants shared views specific of their driving and transit use experience within Florida, the state that many of them had spent most of their senior years. As such, some of the views presented may not be applicable to seniors residing in other parts of the U.S. However, many of the views expressed, were similar to those expressed in numerous published reports on senior mobility challenges during and post driving cessation. As part of the qualitat ive research, it was not possible to solicit the views and experiences of housebound seniors. Such views may have provided additional insights into mobility challenges faced by seniors who are relatively immobile not due to health impairments but to lack of the safety net of family or friends that could assist them in meeting their transportation needs. Another limitation of this research effort was the inability to explicitly discern the size of the population that goes through the driving cessation process and the mobility alternatives available to this group at various stages of the process. Indeed, this deficiency is also related to the lack of not being able to determine transition probabilities during the driving cessation process. 5.11 Future Research Needs An improved estimation in the num bers of former drivers will be dependent on ascertaining the rate of cessation according to gender. This research identified two approaches to derive such estimates; one approach (Foley et al. 2002) involved including active drivers at the point of their deaths in the cessation calculation, the other excluded such drivers and included only thos e who have ceased driving and survive to

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150 tell about it (the majority of research efforts). Foley et al. (2002) acknowledged the differing cessation rates between males and females and also noted the differing mortality rates between males and females. During their study, the male mortality rate was 89 deaths per 1,000 drivers compared to 55 deaths per 1,000 female drivers. Similarly, the cessation rate was 63 per 1,000 male drivers compared to 112 per 1,000 female drivers. Incorporating these mortality and cessation risks resulted in similar cessation rates as well as driving life expectancies for males and females. Additional research is needed to validate the two approaches as to their appropriateness and accuracy in estimating the numbers of former drivers. Further work on deriving transition probabilities in the driving cessation process may yield a better understanding of the transitions during the driving cessation process and subsequent estimates of former drivers. Existing longitudinal datasets, e.g. HRS, may offer a potential resource that could be used to derive such probabilities. There is a need for more collaborative research on senior mobility between transportation engineers/planners and gerontology professionals. Through such collaborative efforts, each discipline may complement the other with additional insights into the mobility challenges facing seniors, thereby enabli ng a wider application and appreciation of ongoing research. Many seniors continue to drive up until the ninth decade of their lives6 (i.e., 80 years and above) and it is during this period (commencing at retirement) of driving transition that marketing and communicating transit services directly to seniors may yield results, as some trips may be amenable to alternatives to the automobile. Research has inferred, that once a senior stops driving due to visual, physical, or cognitive decline, these same impairments that impact activities of daily living make them unlikely to 6 Personal communication with Daniel Foley M.S. (May 2006)

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151 consider transit as a viable transportation option (Burkhardt et al. 2002). Additional research is needed as to what factors enable seniors to consider and subsequently use transit for a proportion of their trips while they still have the option to drive. During 2006, increased ridership on mass transit systems across the U.S. was spurred by rising gasoline prices. Transit agencies are determined to capitalize on this development and retain drivers who had transitioned to their services ( USA Today 10/2/2006). In turn, increasing congestion or high fuel costs may be factors that influence seniors to consider using transit. However, the extent to which these factors influence senior travel behavior is another aspect of needed research that requires clarification. 5.12 Recommendations Recommendations emanating from the research effort can be summarized as follows: Transit service providers must engage in effective transit information awareness campaigns, such as workshops, through personally interacting with potential senior riders. It is not only the availability of information but gaining an understanding how transit can meet their transportation needs and actually using the information provided that can transition seniors into becoming potential transit riders. Transit is not a first choice transportation option for many seniors. However, to increase the chances of senior active drivers considering using transit preand post-driving cessation, there will be a need to inform and train them during the driving reduction phase of their lives. Part of this marketing effort by transit providers will be to engage generational marketing strategies that target a generation rather than an age cohort.

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152 Servicing suburban areas has been a perennial challenge for a number of transit service providers engaged in providing fix ed route services. For any service to be used by seniors, accessibility in terms of getting to the access point must be balanced against the cost of providing and sustaining the service. Senior demands on accessing transit services, preferred traveling times, and their destinations of interest should be ascertained by transit pr operties if envisioned transit services are to have a positive impact on meeting the transportation needs of seniors. For many transit providers, servicing the work trip forms the majority of transit operations. However, there needs to be marketing promotion and demonstration that transit services can be used to facilitate non-work trip purposes. The driving cessation process that many seniors, will face may present opportunities to engage in this type of promotion, so that when permanent cessation is reached, former drivers may consider the transition to transit a viable option (for some trips) and not fear the end of driving as the end of their personal mobility. If transit service is not a viable option for seniors, transit service providers should identify and possibly partner with alternative modes to the POV. One such alternative is the Independent Transportation Network (ITNAmerica), a non-profit transportation service for seniors headquartered in Portland, Maine. This transportation service is based on volunteer drivers of POVs assisting non-driving seniors in meeting their transportation needs. The service is not free to the user; however, the cost is based on per mile driven charge, and payment can be made by cash or through transportation credits (operated like a savings account where charges are deducted as the service is used).

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153 Development of driving cessation management policies should not be seen as accelerating the prospect of license non-renewal but rather enabling senior drivers to better cope with the cessation process through the effective management of it. The implementation of driving cessation managem ent programs offers an opportunity to increase awareness of transportation alternatives to seniors as they manage the driving cessation process. Transit as an alternative to the POV should not be seen as a mode of last resort but a viable option in a basket of alternatives. 5.13 Conclusions The ability to drive is, for many people, highly correlated to their level of enjoyment of life, and this is particularly pertinent to retirees who aim to enjoy their twilight years to the maximum extent possible. Indeed, mobility in recent years has reached unprecedented levels such that seniors are experiencing “longer, happier, fuller lives than their counterparts today and certainly than the elderly of just a few decades ago” (Rosenbloom 2004, p. 3). The senior transit market assessment indicate a modest growth in transit market attributable to seniors and the focus group sessions elicited confirmation of the inextricable link between personal well-being and mobility. The limited use made of existing transit services by seniors today is influenced by the ability to drive, level of service accessibility and frequency, and a general non-interest in transit services; for the majority of seniors, transit does not meet their transportation needs at a level and flexibility that is found with POV transportation. This research highlights the importance of understanding the process of driving cessation, and the transportation needs of seniors at the present time has become increasingly pertinent, warranting additional research as there are currently several issues that continue to directly impact levels of senior mobility. In recent decades, there

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154 has been evidence of decreasing family size, fewer adult children per senior adult, greater spatial separation of seniors between their adult children, and seniors preferring to “age in place.” All these factors signific antly affect the evolving role of the family versus institutional support in meeting senior needs. Will the family remain the primary “safety net” for seniors in future decades? Indeed, the potential reluctance of senior and former drivers to utilize alternative non-automobile transportation modes, e.g., fixed route transit, has been partly influenced by negative perceptions and a non-interest of transit services developed over a number of years. Noting these negative perceptions of public transportation, the elderly may feel that after driving for many years, “they deserve [and will expect] better” (Shope 2003, p. 58). Tr ansit providers have extensive work to do to change the perceptions of transit service provision and subsequently encourage the use of such services by senior populations in forthcoming generations if transit is to become a viable transportation alternative for those seniors ceasing to drive.

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155 REFERENCES 2004 Florida Statutes. Title XXIII Motor Vehicles. Chapter 322 Drivers’ Licenses http://www.leg.state.fl.us/statutes/in dex.cfm?mode=View%20Statutes&SubMenu =1&App_mode=Display_Statute&Search_String=&URL=CH0322/Sec18.HTM. AAA (2005). Senior Licensing Laws. AAA Public Affairs http://www.aaapublicaffairs.com/Assets/Files/20061301546440. StateSeniorLicensingLaws.doc. AARP (2005). Reimagining America – AARP’s Blue for the Future. AARP. Washington DC. http://0-assets.aarp.org.mill1.sjlibrary.org/www.aarp.org_/articles/ legpolicy/reimagining_200601.pdf. Adler, G., Rottunda, S., Baueer, M., & Kuskowski, M. (2000). The Older Driver with Parkinson’sDisease. Journal of Gerontological Social Work. Volume 32. Issue 2. pp. 39-49. Adler, G., Rottunda, S., Kuskowski, M. (1999). Dementia and Driving: Perceptions and Changing Habits. Clinical Gerontologist Volume 20, Issue 2, pp. 23-34. Anderson, R.N. (1991). Method for Constructing Complete Annual U.S. Life Tables. National Center for Health Statistics. Vital Health Statistics Volume 21.No. 129. Arias, E. (2002). United States Life Tables 2000. National Vital Statistics Reports. Volume 51. No 3. National Center for Health Statistics. Hyattsville, MD. Arias, E. (2006). United States Life Tables, 2003. National Vital Statistics Reports. Volume 54. No 14. National Center for Health Statistics. Hyattsville, MD. Bauer, M.J. & Rottunda, S. (2003). Older Women and Driving Cessation. Qualitative Social Work, Volume 2, pp. 309-325.

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159 Hebert, K., Martin-Cook, K., Svetlik, D.A., & Weiner, M.F. (2002). Caregiver DecisionMaking and Driving: What We Say versus What We Do. Clinical Gerontologist, Volume 26, Issue 1. Hobbs, F.B. & Damon, B.L. (1996). 65+ in the United States Current Population Reports: Special Studies. Report P23-190. U.S. Government Printing Office, Washington, DC. Horowitz, A., Boerner, K., & Reinhardt, J. P. (2002). Psychosocial Aspects of Driving Transitions in Elders with Low Vision. Gerontechnology, Volume 1, Issue 4, pp. 262-273. ICF Consulting. (2006). Estimating the Impacts of the Aging Population on Transit Ridership. Report# NCHRP 20-65 (4) Transportation Research Board, Washington DC. http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w86.pdf. Janke, M.K. (1994). Age-related Disabilities that may Impair Driving and their Assessment. Sacramento: California State Department of Motor Vehicles, National Highway Safety Administration. Jett, K., Tappen, R.M., & Rosselli, M. (2005). Imposed versus involved: Different Strategies to Effect Driving Cessation in Cognitively Impaired Older Adults. Geriatric Nursing, Volume 26, Issue 2, pp. 111-6. Kington, R., Reuben, D., Rogowski, J., & Lillard, L. (1994). Sociodemographic and Health Factors in Driving Patterns After 50 Years of Age. American Journal of Public Health, Volume 84, Issue No. 8, pp. 1327-1329. Kostyniuk, L.P., & Shope, J.T. (2003). Driving and Alternatives: Older Drivers in Michigan. Journal of Safety Research, Volume 34, Issue 4, pp. 407-414. Lange, J.E. & McKnight, A.J. (1996). Age-Based Road Test Policy Evaluation. Transportation Research Record. Volume 1550. pp. 81-87. Washington, DC. Levy, D.T. (1995). The Relationship of Age and State License Renewal Policies to Driving Licensure Rates. Accident Analysis and Prevention, Volume 27, Issue 4, pp. 461-467.

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160 Lin, G., and Rogerson, P.A. (1995). Elderly Parents and the Geographic Availability of Their Adult Children. Research on Aging. Volume 17. No. 3. pp.303-331. Longino, C.F. Jr., Perzynski, A.T., Stoller, E.P. (2002). Pandora’s Briefcase: Unpacking the Retirement Migration Decision. Research on Aging, Volume 24, Issue 1, pp. 29-49. Manton, K.G., and Land, K.C. (2000). Active Life Expectancy Estimates for U.S. Elderly Population: Multidimensional Continuous Mixture Model of Functional Change Applied to Completed Cohorts, 1982 to 1996. Demography Volume 37. Issue 3. pp.253-65. Marottoli, R.A., Mendes de Leon, C.F.M., Glass, T.A., Williams C.S., Cooney, L.M., Jr. & Berkman L.F. (2000). Consequences of Driving Cessation: Decreased Out-ofHome Activity Levels Journal of Gerontology Series B Psychology Science Social Science, Volume 55, pp. 334-340. McKnight, J. (2003). The Freedom of the Open Road: Driving and Older Adults. Generations. The Journal of the American Society on Aging, Volume 27, No. 2, pp. 25 -3. Messinger-Rapport, B.J. & Rader, E. (2000). High Risk on the Highway: How to Identify and Treat the Impaired Older Driver Geriatrics, Volume 55, No. 10, pp. 32-45. Molnar, L.J. & Eby, D.W. (2005). A Brief Look at Driver License Renewal Policies in the United States. Public Policy & Aging Report. National Academy on an Aging Society, Volume 15, Issue 2, pp. 1, 13-17. Myers, D. (1996). Changes over Time in Transportation Mode for Journey to Work: Effects of Aging and Immigration. Decennial Census Data for Transportation Planning: Case Studies and Strategies for 2000, Volume 2. TRB Conference Proceedings, Volume 13. National Academy Press, Washington, DC. National Center For Health Statistics (2002) Work Table 1. Deaths from each cause, by 5-year age groups, race and sex, United States, 2000. http://www.cdc.gov/nchs/data/dvs/wktbli.pdf.

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161 National Center for Health Statistics. (2005). Health, United States 2005 with Chartbook on Trends in the Health of Americans U.S. Department of Health and Human Services. Hyattsville, MD. Nelson, D.E., Sacks, J.J., & Chorba, T.L. (1992). Required Vision Testing for Older Drivers. New England Journal of Medicine, Volume 326, pp. 1784-1785. Office of Highway Policy Information Federal Highway Administration (2001). Highway Statistics 2000 Licensed drivers, by State, Sex, and Age Group Table DL-22. http://www.fhwa.dot.gov/ohim/hs00/pdf/dl22.pdf. Office of Highway Policy Information Federal Highway Administration (2005). Highway Statistics 2004 http://www.fhwa.dot.gov/policy/ohim/hs03/index.htm. Owsley, C., Ball, K., McGwin, G., Sloane, M.E., Roenker, D.L., White, M.F., & Overley, T. (1998). Visual Processing Impairment and Risk of Motor Vehicle Crash Among Older Adults Journal of the American Medical Association, Volume 279, No. 14. Persson, D. (1993). The Elderly Driver: Deciding when to Stop. Gerontologist, Volume 33, Issue 1, pp. 88-91. Polzin, S. & Page, O. (2006). Transit Use Viability Among Older Drivers Losing Driving Privileges. National Center for Transit Research. Tampa, FL. http://www.nctr.usf.edu/publications.htm. Polzin, S., & Chu, X. (2005). Public Transit in America: Results from the 2001 National Household Travel Survey National Center for Transit Research. Tampa, FL. http://www.nctr.usf.edu/pdf/527-09.pdf. Ralston, L.S., Bell, S. L., Mote, J.K., Rainey, T.B., Brayman, S., & Shotwell, M. (2001). Giving Up the Car Keys: Perceptions of Well Elders and Families. Physical & Occupational Therapy in Geriatrics Volume 19. Issue 4. pp. 59-70. Rand Center for the Study of Aging (2006). Rand HRS Data Documentation, Version F, Labor & Population Program, Rand Center for the Study of Aging, Santa Monica CA http://www.rand.org/labor/aging/dataprod/randhrsf.pdf.

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162 Rock, S.M. (1998). Impact from Changes in Illinois Drivers License Renewal Requirements for Older Drivers. Accident Analysis and Prevention Volume 30, Issue 1, pp. 69-74. Elsevier Science Limited. Rogerson, P.A., Burr, J.A., & Lin, G. (1997). Changes in Geographic Proximity between Parents and their Adult Children. International Journal of Population Geography Volume 31. pp.121-136. Rosenbloom, S. & Fielding, G.J. (1998). Transit Markets of the Future – The Challenge of Change. National Academy Press. Washington DC. Rosenbloom, S. (2001). Driving Cessation Among Older People, When Does it Happen and What Impact Does it Have? Transportation Research Record. No. 1779. pp.93-99. Rosenbloom, S. (2003). Older Drivers. Should We Test Them Off the Road? Access Number 23, pp. 8-13. University of California Transportation Center. Rosenbloom, S. (2004). Mobility of the Elderly: Good News and Bad News Transportation in an Aging Society A Decade of Experience Technical Papers and Reports from a Conference November 7–9, 1999 Bethesda, Maryland, Transportation Research Board Washington, DC. Shope, J.T. (2003). What Does Giving Up Driving Mean to Older Drivers, and Why Is It So Difficult? Generations. The Journal of the American Society on Aging, Volume 27, Issue 2, pp. 57-59. Silverstein, M. and Angelelli, J.J. (1998). Older Parents’ Expectations of Moving Closer to Their Children. Journal of Gerontology Volume 53B. No. 3. pp.S153-163. Singer, J.D., & Willet, J.B. (2003). Applied Longitudinal Data Analysis – Modeling Change and Event Occurrence. Oxford University Press. New York. Stamatiadis, N., Agent, K.R., & Rideway, M. (2003). Driver License Renewal for the Elderly: A Case Study. The Journal of Applied Gerontology, Volume 22, Issue 1, pp. 42-56.

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163 Stanfield, R. (1996). Aging of America. National Journal, Volume 7, Issue 20, pp. 15781583. Staplin, L. & Freund, K. (2005). Public and Private Policy Initiatives to Move Seniors Forward. Public Policy & Aging Report. National Academy on an Aging Society, Volume 15, Issue 2, pp. 1, 3-5. Stewart, R., Moore, M., Marks, R., May, F., and others. (1993). Driving Cessation in the Elderly: An Analysis of Symptoms, Diseases, and Medications. J ournal of Geriatric Drug Therapy, Volume 8, Issue 2, pp. 45-60. Straight, A. (1997). Community Transportation Survey Research Report. AARP Public Policy Institute. http://assets.aarp.org/rgcenter/il/d16603_commtran.pdf. Taylor, B.D. & Tripodes, S. (2001). The Effects of Driving Cessation on the Elderly with Dementia and Their Caregivers. Accident Analysis & Prevention, Volume 33, pp. 519-528. Transportation Research Board (2005). Safe Mobility for Older Americans – Report of the Committee for the Conference on Transportation in an Aging Society. Transportation Research Board. Washington DC. http://www.trb.org/publications/conf/CPW2.pdf. U.S. Bureau of the Census (1996). Report WP/96, World Population Profile. U.S. Government Printing Office, Washington, DC. http://www.census.gov/ipc/prod/wp96/wp96.pdf. U.S. Census Bureau (2004). Projected Population of the United States, by Age, Sex, Race and Hispanic Origin. U.S. Census Bureau, Population Division, Population Projections Branch. http://www.census .gov/ipc/www/usinterimproj/usproj20002050.xls. U.S. Census Bureau (2005). DP-1. Profile of General Demographic Characteristics: 2000. U.S. Census Bureau. http://www.census.gov/census2000/states/us.html. U.S. Census Bureau: International Database. Generated by Oliver Page. http://www.census.gov/ipc/www/idbnew.html (11 September 2006).

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164 U.S. Department of Transportation & Bureau of Transportation Statistics (2003). NHTS 2001 Highlights Report BTS03-0. Washington DC. U.S. Department of Transportation (2004). 2001 National Household Travel Survey User’s Guide Version 3 – National Sample with Add-Ons. Washington DC. http://nhts.ornl.gov/2001/usersguide/index.shtml. U.S. Department of Transportation (2004). 2001 National Household Travel Survey User’s Guide. http://nhts.ornl.gov/2001/usersguide/index.shtml. USA Today (2006). Mass Transit Still Hot After $3 Gas. Monday 2nd October 2006. p.5B. Waldorf, B. & Pitfield, D. (2005). The Effects of Demographic Shifts on Non-Automobile Transportation Needs of the Elderly. In: Donaghy, K., Poppelreuter, S. and Rudinger, G. (eds.) Social Dimensions of Sustainable Transport: Transatlantic Perspectives London, Ashgate, pp. 67-89. Waldorf, B. (2001). Anticipated Mode Choice Following Driving Cessation. European Research in Regional Science, Volume 11, pp. 22-40. Waldorf, B. (2003). Automobile Reliance Among the Elderly: Race and Spatial Context Effects. Growth and Change Volume 34, Issue 2, pp.175-201. Waller, P.F. (1991). The Older Driver. Human Factors, Volume 33, No. pp.499-505. Walser, N. (1991). When to Hang up the Keys Aged Drivers. Harvard Health Letter. http://www.findarticles.com/p/articles/mi_m1585/is_n1_v17/ai_11514877. Wang, C. & Carr, D. (2004). Older Driver Safety: A Report from the Older Drivers Project. J ournal of the American Geriatric Society, Volume 52, pp. 143-9. Yassuda, M.S., Wilson, J.J., & von Mering, O. (1997). Driving Cessation: The Perspective of Senior Drivers. Educational Gerontology, Volume 23, Issue 6, pp. 525-538.

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165 APPENDICES

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166 Appendix A List of Acronyms Table A.1 List of Acronyms AAA American Automobile Association ACC Adaptive Cruise Control Systems ADL Activities of Daily Living AHEAD Assets and Health dynamics Among the Oldest Old BTS Bureau of Transportation Statistics CATI Computer-Aided Telephone Interview CDC Centers for Disease Control CUTR Center for Urban Transportation Research FHWA Federal Highway Administration GPS Global Positioning Systems HRS Health and Retirement Study ITNAmerica Independent Transportation Network America ITS Intelligent Transportation System NCHS National Center for Health Statistics NHTS National Household Travel Survey NHTSA National Highway Traffic Safety Administration NIA National Institute of Aging NPTS Nationwide Personal Travel Survey NSFH National Survey of Families and Households OHPI Office of Highway Policy Information POV Personally-Operated Motor Vehicle PUMS Public Use Microdata Sample

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167 Appendix A (Continued) RDD Random Digit Dialing RV Recreational Vehicle SIPP Survey of Income and Program Participation SUV Sport Utility Vehicle TRB Transportation Research Board TRIS Transportation Research Information Services USDOT U.S. Department of Transportation USF University of South Florida

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168 Appendix B Driving Cessation Estimates for Older Males and Females Waldorf (2001) Table B.1 Driving Cessation Estimates (Males) Waldorf (2001) Scenario 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Proportion currently licensed*, pcl 0.940.910.87 0.75 Base Case Proportion currently driving**, pcd 0.880.850.77 0.54 Proportion ever-licensed, pel = pcl 0.940.910.87 0.75 Scenario 1 Proportion stopped driving, p* 0.060.070.11 0.28 Scenario 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Proportion currently licensed*, pcl 0.940.910.87 0.75 Base Case Proportion currently driving**, pcd 0.880.850.77 0.54 Proportion ever-licensed, pel = 1 1.001.001.00 1.00 Scenario 2 Proportion stopped driving, p* 0.120.150.23 0.46 Scenario 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Proportion currently licensed*, pcl 0.940.910.87 0.75 Base Case Proportion currently driving**, pcd 0.880.850.77 0.54 Proportion ever-licensed, pel = (pcl + 1) 0.970.960.94 0.88 Scenario 3 Proportion stopped driving, p* 0.090.110.18 0.38 OHPI/FHWA ** AHEAD

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169 Appendix B (Continued) Table B.2 Driving Cessation Estimates (Females) Waldorf (2001) Scenario 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Proportion currently licensed*, pcl 0.740.640.49 0.26 Base Case Proportion currently driving**, pcd 0.700.600.44 0.22 Proportion ever-licensed, pel = pcl 0.940.910.87 0.75 Scenario 1 Proportion stopped driving, p* 0.260.340.49 0.71 Scenario 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Proportion currently licensed*, pcl 0.740.640.49 0.26 Base Case Proportion currently driving**, pcd 0.700.600.44 0.22 Proportion ever-licensed, pel = 1 1.001.001.00 1.00 Scenario 2 Proportion stopped driving, p* 0.300.400.56 0.78 Scenario 70 – 74yrs 75 – 79yrs 80 – 84yrs 85yrs+ Proportion currently licensed*, pcl 0.740.640.49 0.26 Base Case Proportion currently driving**, pcd 0.700.600.44 0.22 Proportion ever-licensed, pel = (pcl + 1) 0.870.820.75 0.63 Scenario 3 Proportion stopped driving, p* 0.200.270.41 0.65 OHPI/FHWA ** AHEAD

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170 Appendix C Complete Life Tables 2000 (Source: National Center for Health Statistics, 2002) Table C.1 Life Table for Males: United States 2000

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171 Appendix C (Continued) Table C.1 ( Continued )

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172 Appendix C (Continued) Table C.2 Life Table for Females: United States 2000

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173 Appendix C (Continued) Table C.2 ( Continued )

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174 Appendix D Calculation of Life Tables for Persons Ages 35 and Older (Base Year 2000) A life table is defined as “a statistical table that follows a hypothetical cohort of 100,000 persons born at the same time as they progress through successive ages, with the cohort reduced from one age to the next according to a set of death rates by age until all persons eventually die” (U.S. Census Bureau, 1996). A life table thus defined is technically referred to as a “Period” Life Table (synthetic population) versus a “Cohort” Life table, which follows the life experience of an actual birth cohort. In addition, a life table can be “abridged” (data grouped by 5 or 10 year age intervals) or “complete” (i.e., data for individual years). Life tables fo r the U.S. are produced annually by the National Center for Health Statistics (NCHS) a unit of the Centers for Disease Control (CDC). The creation of an abridged life table for persons 35 years and older (base year 2000) is described as follows. The construction of the life table will follow the methodology as provided for by the CDC (Anderson 1999). The foundation of any life table is to derive the probability of dying (the opposite of which is the probability of surviving), as the “probability of dying forms the basis of the life table: all subsequent columns are derived from it.” (Arias, 2002 p.2) To determine the probability of dying (xq), estimates of the incidence of death at each respective age grouping are obtained. Table D.1 illustrates observed death rates in the year 2000 for males and females respectively.

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175 Appendix D (Continued) Table D.1 Male and Female Death Rates Year 2000 Population Deaths Deaths per Capita Year 2000 Males Females Males Females Males Females 35 39yrs 11,276,704 11,339,80 223,25212,8880.002062 0.001137 40 44yrs 11,168,659 11,353,88 334,04519,6130.003048 0.001727 45 49yrs 9,955,867 10,270,55 845,12125,7110.004532 0.002503 50 54yrs 8,706,148 9,083,51 955,27734,2320.006349 0.003769 55 59yrs 6,553,094 7,005,93 364,42542,3260.009831 0.006041 60 64yrs 5,165,683 5,699,02 678,89655,1990.015273 0.009686 65 69yrs 4,402,844 5,131,11 1103,93577,8040.023606 0.015163 70 74yrs 3,904,321 4,945,625 143,473115,9970.036747 0.023454 75 79yrs 3,051,227 4,374,151 173,327164,3730.056806 0.037578 80 84yrs 1,853,795 3,130,873 166,892195,8530.090027 0.062555 85 89yrs 884,151 1,918,650 128,877206,9360.145764 0.107855 90 94yrs 286,369 837,41564,439154,8440.225021 0.184907 95 99yrs 58,970 231,00518,55266,0890.314601 0.286093 100yrs+ 10,020 40,7202,87415,5600.286826 0.382122 Total 67,277,852 75,362,2711,103, 3851,187,4250.016400 0.015756 Sources: Projected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) U.S. Census Bureau, Population Division, Population Projec tions Branch http://www.cens us.gov/ipc/www/usinterimproj/usproj2000-2050.xls GMWK I Total deaths for each cause by 5-year age gr oups, United States, 1999-2003. National Center for Health Statistics. http://www.cdc.gov/nchs/data/dvs/wktbli.pdf According to the CDC guideline, xq, is determined by the following: x x x xd l d q 2 1 where xdnumber of deaths occurring between age x and1 x, and xlis the life table population at risk of dying between ages x and1 x. Formula (1) assumes that the age intervals are 1 year of age in length. Additionally, the formula cannot be used on the last line, however, as death is certain, the probability of dying at 100yrs+ is given as 1. As an abridged life table is being created formula (1) has to be adjusted to reflect the groupings of the years in 5 year intervals, indicated in formula (2) and the results are presented in Table D.2. ( 1 )

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176 Appendix D (Continued) 5 2 5 x x x xd l d q Table D.2 Male and Female Probabilities of Dying (xq) Year 2000 Population (xl) Deaths (xd) Probability of Dying (xq) Year 2000 Males Females Males Females Males Females 35 39yrs 11,276,704 11,339,80223,25212,888 0.010257 0.005667 40 44yrs 11,168,659 11,353,88334,04519,613 0.015126 0.008600 45 49yrs 9,955,867 10,270,55845,12125,711 0.022407 0.012439 50 54yrs 8,706,148 9,083,51955,27734,232 0.031250 0.018667 55 59yrs 6,553,094 7,005,93364,42542,326 0.047977 0.029758 60 64yrs 5,165,683 5,699,02678,89655,199 0.073557 0.047284 65 69yrs 4,402,844 5,131,111103,93577,804 0.111454 0.073047 70 74yrs 3,904,321 4,945,625143,473115,997 0.168277 0.110777 75 79yrs 3,051,227 4,374,151173,327164,373 0.248708 0.171756 80 84yrs 1,853,795 3,130,873166,892195,853 0.367438 0.270477 85 89yrs 884,151 1,918,650128,877206,936 0.534164 0.424747 90 94yrs 286,369 837,41564,439154,844 0.720043 0.632261 95 99yrs 58,970 231,00518,55266,089 0.880494 0.833978 100yrs+ 10,020 40,7202,87415,560 1.000000 1.000000 To determine the numbers of persons dying in a particular cohort, it follows that with an initial synthetic male population ages 35 to 39 years of 100,000, 1,026 of this cohort will not see their 40th birthday (i.e., 100,000 x 0.010257 (for (xq) see Table D.2)). Thus, 98,974 will enter the second age interval, namely 40 to 44 years. The process is continued applying the respective xq for each cohort. The Person Years lived, xLis determined by the following formula: x x xd l L 2 1 where, xlis the life table population at risk of dying between ages x and1 x, and ( 2 ) ( 3 )

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177 Appendix D (Continued) xdnumber of deaths occurring between age x and1 x. Again as we are preparing an abridged life table the formula is adjusted to reflect the 5 year groupings as indicated in formula 4. x x xd l L 2 1 5 If all the persons in a cohort (xl) had lived to progress to the next cohort (1 xl), the maximum number of person years li ved would be 5 years multiplied byxl. Unfortunately, this is not the case (as deaths at all ages is inevitable), and to take account of those persons who died at sometime in their respective cohort, we assume that each made it half-way through the age interval (indicated by xd 2 1). Total person years lived (xT) represents the total number of person-years that would be lived after the beginning of the age interval x to 1 x by the synthetic life table cohort and indicated by the following formula. 0t t x xL T In other words, for the initial 100,000 males ages 35 to 39 years, xT = 4,135,932 (i.e., the cumulative sum of all xL for each cohort). For the next cohort (40 to 44 years), xT= 3,638,496 (which is 4,135,932 less xL for the cohort 35 to 39 years). The process is continued deducting the respective xL for each cohort. ( 4 ) (5)

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178 Appendix D (Continued) Life expectancy (xe) for a cohort is determined by the following formula: x x xl T e where, xTrepresents the total number of person-years that would be lived after the beginning of the age interval x to1 x, and xlis the life table population at risk of dying between ages x and1 x. The resulting period/abridged life tables for males and females respectively are presented in Tables D.3 and D.4. Table D.3 Life Table for Males: United States 35yrs+, 2000 Cohort Population (xl) Probability of Dying (xq) Deaths (xd) Person Years Lived (xL) Person Years Lived Total (xT) Life Expectancy (xe) 35 39yrs 100,000 0.010257 1,026497,4364,135,932 41.36 40 44yrs 98,974 0.0151261, 497491,1293,638,496 36.76 45 49yrs 97,477 0.0224072, 184481,9263,147,368 32.29 50 54yrs 95,293 0.0312502, 978469,0212,665,442 27.97 55 59yrs 92,315 0.0479774, 429450,5032,196,421 23.79 60 64yrs 87,886 0.0735576, 465423,2691,745,918 19.87 65 69yrs 81,422 0.1114549, 075384,4211,322,648 16.24 70 74yrs 72,347 0.16827712,174331,298938,228 12.97 75 79yrs 60,172 0.24870814,965263,449606,929 10.09 80 84yrs 45,207 0.36743816,611184,508343,480 7.60 85 89yrs 28,596 0.53416415,275104,794158,972 5.56 90 94yrs 13,321 0.7200439,59242,62654,178 4.07 95 99yrs 3,729 0.8804943,28410,43811,552 3.10 100yrs+ 446 1.0000004461,1141,114 2.50 (6)

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179 Appendix D (Continued) Table D.4 Life Table for Females: United States 35yrs+, 2000 Cohort Population (xl) Probability of Dying (xq) Deaths (xd) Person Years Lived (xL) Person Years Lived Total (xT) Life Expectancy (xe) 35 39yrs 100,000 0.005667567498,5834,584,834 45.85 40 44yrs 99,433 0.008600855495,0294,086,250 41.10 45 49yrs 98,578 0.0124391,226489,8263,591,222 36.43 50 54yrs 97,352 0.0186671,817482,2173,101,396 31.86 55 59yrs 95,535 0.0297582,843470,5662,619,179 27.42 60 64yrs 92,692 0.0472844,383452,5022,148,613 23.18 65 69yrs 88,309 0.0730476,451425,4181,696,111 19.21 70 74yrs 81,858 0.1107779,068386,6221,270,692 15.52 75 79yrs 72,790 0.17175612,502332,696884,071 12.15 80 84yrs 60,288 0.27047716,307260,674551,374 9.15 85 89yrs 43,982 0.42474718,681173,205290,700 6.61 90 94yrs 25,301 0.63226115,99786,511117,495 4.64 95 99yrs 9,304 0.8339787,75927,12230,983 3.33 100yrs+ 1,545 1.0000001,5453,8623,862 2.50

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180 Appendix E Calculation of Survivor Curves xSand xS*for Persons Ages 35 and Older (Base Year 2000) Calculation of survivor curves for the y ear 2030, males and females respectively, is a continuation of the life table process. The life tables generated in this study are presented in Tables E.1 and E.2. Table E.1 Abridged Life Table for Males: United States 35yrs+, 2000 Cohort Population (xl) Probability of Dying (xq) Deaths (xd) Person Years Lived (xL) Person Years Lived Total (xT) Life Expectancy (xe) 35 39yrs 100,000 0.010257 1,026497,4364,135,932 41.36 40 44yrs 98,974 0.0151261, 497491,1293,638,496 36.76 45 49yrs 97,477 0.0224072, 184481,9263,147,368 32.29 50 54yrs 95,293 0.0312502, 978469,0212,665,442 27.97 55 59yrs 92,315 0.0479774, 429450,5032,196,421 23.79 60 64yrs 87,886 0.0735576, 465423,2691,745,918 19.87 65 69yrs 81,422 0.1114549, 075384,4211,322,648 16.24 70 74yrs 72,347 0.16827712,174331,298938,228 12.97 75 79yrs 60,172 0.24870814,965263,449606,929 10.09 80 84yrs 45,207 0.36743816,611184,508343,480 7.60 85 89yrs 28,596 0.53416415,275104,794158,972 5.56 90 94yrs 13,321 0.7200439,59242,62654,178 4.07 95 99yrs 3,729 0.8804943,28410,43811,552 3.10 100yrs+ 446 1.0000004461,1141,114 2.50

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181 Appendix E (Continued) Table E.2 Abridged Life Table for Females: United States 35yrs+, 2000 Cohort Population (xl) Probability of Dying (xq) Deaths (xd) Person Years Lived (xL) Person Years Lived Total (xT) Life Expectancy (xe) 35 39yrs 100,000 0.005667567498,5834,584,834 45.85 40 44yrs 99,433 0.008600855495,0294,086,250 41.10 45 49yrs 98,578 0.0124391,226489,8263,591,222 36.43 50 54yrs 97,352 0.0186671,817482,2173,101,396 31.86 55 59yrs 95,535 0.0297582,843470,5662,619,179 27.42 60 64yrs 92,692 0.0472844,383452,5022,148,613 23.18 65 69yrs 88,309 0.0730476,451425,4181,696,111 19.21 70 74yrs 81,858 0.1107779,068386,6221,270,692 15.52 75 79yrs 72,790 0.17175612,502332,696884,071 12.15 80 84yrs 60,288 0.27047716,307260,674551,374 9.15 85 89yrs 43,982 0.42474718,681173,205290,700 6.61 90 94yrs 25,301 0.63226115,99786,511117,495 4.64 95 99yrs 9,304 0.8339787,75927,12230,983 3.33 100yrs+ 1,545 1.0000001,5453,8623,862 2.50 Calculation of xSis a straight forward division of cohort xl by a cohort 30 years later30 xl, (i.e., the proportion of cohort xl surviving 30 years later 30 xl) For example, it is assumed that the male cohort 40 to 44 years in 2000 will become the cohort 70 to 74 years in 2030. In this case xl in 2000 approximated 98,974 persons and in 2030, 30 xlapproximated 72,347 persons. Thus 26,627 persons of the original cohort died at some time during the intervening years, leaving 72,347 persons (or 73 percent) who will reach at least their 70th birthday in 2030. The resulting survivor probabilitiesxS are presented in Tables E.3 and E.4.

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182 Appendix E (Continued) Table E.3 Survival Probabilities xSfor Males: United States 35yrs+, 2030 Cohort xl (yr 2000) Cohort 30 xl (yr 2030) 30 x x xl l S 35 39yrs 100,00065 69yrs81,422 0.814215 40 44yrs 98,97470 74yrs72,347 0.730965 45 49yrs 97,47775 79yrs60,172 0.617298 50 54yrs 95,29380 84yrs45,207 0.474401 55 59yrs 92,31585 89yrs28,596 0.309768 60 64yrs 87,88690 94yrs13,321 0.151573 65 69yrs 81,42295 99yrs3,729 0.045803 70 74yrs 72,347100yrs+446 0.006160 Table E.4 Survival Probabilities xSfor Females: United States 35yrs+, 2030 Cohort xl (yr 2000) Cohort 30 xl (yr 2030) 30 x x xl l S 35 39yrs 100,00065 69yrs88,309 0.883090 40 44yrs 99,43370 74yrs81,858 0.823248 45 49yrs 98,57875 79yrs72,790 0.738402 50 54yrs 97,35280 84yrs60,288 0.619280 55 59yrs 95,53585 89yrs43,982 0.460373 60 64yrs 92,69290 94yrs25,301 0.272953 65 69yrs 88,30995 99yrs9,304 0.105357 70 74yrs 81,858100yrs+1,545 0.018870 Surviving and driving probabilities take into account the preponderance of driving cessation. In order to derive revisedxS, (i.e.,xS*), the driving cessation probabilities are applied to the probability of dying (xq) to generate revisedxl, which is the life table population at risk of dying between ages x and1 x. The following formula7 is used to apply the cessation probabilities: ) 1 ( ** x x xq cp q q (1) 7 Personal communication with Dr. B. Waldorf

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183 Appendix E (Continued) where xq* adjusted probability of dying taking into account cessation probability, xq probability of dying and cpcessation probability. This formula derives the probability of dying or surviving and driving. The revised xl are presented in Tables E,5 and E.6 for males and females respectively. Table E.5 Revised Male Population at Risk of Dyingxl: United States 35yrs+, 2000 Cohort Probability of Dying (xq) Cessation Probability (cp) Revised Probability of Dying (xq*) Revised Population (xl) 35 39yrs 0.0102570.0000000.010257 100,000 40 44yrs 0.0151260.0000000.015126 98,974 45 49yrs 0.0224070.0000000.022407 97,477 50 54yrs 0.0312500.0000000.031250 95,293 55 59yrs 0.0479770.0000000.047977 92,315 60 64yrs 0.0735570.0000000.073557 87,886 65 69yrs 0.1114540.0500000.155881 81,422 70 74yrs 0.1682770.0500000.209863 68,729 75 79yrs 0.2487080.1000000.323837 54,306 80 84yrs 0.3674380.1000000.430694 36,719 85 89yrs 0.5341640.4000000.720498 20,905 90 94yrs 0.7200430.5000000.860021 5,843 95 99yrs 0.8804941.0000001.000000 818 100yrs+ 1.0000001.0000001.000000 0

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184 Appendix E (Continued) Table E.6 Revised Female Population at Risk of Dying xl: United States 35yrs+, 2000 Cohort Probability of Dying (xq) Cessation Probability (cp) Revised Probability of Dying (xq*) Revised Population (xl) 35 39yrs 0.0056670.0000000.005667 100,000 40 44yrs 0.0086000.0000000.008600 99,433 45 49yrs 0.0124390.0000000.012439 98,578 50 54yrs 0.0186670.0000000.018667 97,352 55 59yrs 0.0297580.0000000.029758 95,535 60 64yrs 0.0472840.0000000.047284 92,692 65 69yrs 0.0730470.0500000.119395 88,309 70 74yrs 0.1107770.1000000.199699 77,765 75 79yrs 0.1717560.2000000.337404 62,236 80 84yrs 0.2704770.2000000.416382 41,237 85 89yrs 0.4247470.8000000.884949 24,067 90 94yrs 0.6322610.9900000.996323 2,769 95 99yrs 0.8339781.0000001.000000 10 100yrs+ 1.0000001.0000001.000000 0 As before calculation of xS*is a straight forward division of cohort xl by a cohort 30 years later30 xl. The resulting survivor probabilitiesxS* are presented in Tables E.7 and E.8. Table E.7 Survival Probabilities xS* for Males: United States 35yrs+, 2030 Cohort xl (yr 2000) Cohort 30 xl (yr 2030) 30 x x xl l S 35 39yrs 100,000 65 69yrs 81,422 0.814215 40 44yrs 98,974 70 74yrs 68,729 0.694417 45 49yrs 97,477 75 79yrs 54,306 0.557111 50 54yrs 95,293 80 84yrs 36,719 0.385332 55 59yrs 92,315 85 89yrs 20,905 0.226448 60 64yrs 87,886 90 94yrs 5,843 0.066482 65 69yrs 81,422 95 99yrs 818 0.010045 70 74yrs 68,729 100yrs+ 0 0.000000

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185 Appendix E (Continued) Table E.8 Survival Probabilities xS* for Females: United States 35yrs+, 2030 Cohort xl (yr 2000) Cohort 30 xl (yr 2030) 30 x x xl l S 35 39yrs 100,000 65 69yrs 88,309 0.883090 40 44yrs 99,433 70 74yrs 77,765 0.782086 45 49yrs 98,578 75 79yrs 62,236 0.631333 50 54yrs 97,352 80 84yrs 41,237 0.423588 55 59yrs 95,535 85 89yrs 24,067 0.251916 60 64yrs 92,692 90 94yrs 2,769 0.029872 65 69yrs 88,309 95 99yrs 10 0.000115 70 74yrs 77,765 100yrs+ 0 0.000000

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186 Appendix F Recalculation of Licensing Proportions for Persons Ages 85 and Older in 2030 (Base Year 2000) In complying with the age groupings originally established in this study, persons ages 85+ years in 2030 would have been ages 55+ years in 2000. Table F.1 revisits the population and licensing data for this cohort. However, amalgamating all persons 55+ years in 2000 would hide important licensing proportions for persons ages 85 years or more which might have an impact on the licen sing patterns of the “oldest-old” grouping in 2030. It is therefore prudent to disaggregate the cohort 55+ years in 2000 to smaller grouping where data permits. Table F.1 Population and Licensing Statistics for the 85year+ Cohort Year 2000 (Actual) Year 2030 (Estimated) Population Licensed Males Females Males Females Population 26,170,47433,314, 5093,339,937 6,263,097 Licensed 24,626,77725,374, 1523,142,927 4,770,317 Licensed proportion (% ) 94.10%76.17%94.10% 76.17% Sources: Office of Highway Policy Information, 2001 & Pr ojected Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) U.S. Census Bureau, Po pulation Division, Populat ion Projections Branch In the year 2000, the Office of Highway Po licy Information (OHPI) provided licensing data for several cohorts (grouped in 5 year intervals) above 55 years, persons ages 85+ years were grouped together as the last cohort. Assuming that the majority of seniors will die before their 100th birthday, it is possible with the year 2000 OHPI and census data to derive licensing proportions for persons ages 65 to 100 years in 2030. In this case, the last cohort in 2000 that will be of interest here, will be those ages 70 to 75 years. The licensing proportions of persons 55 years and older in 2000 grouped by 5 year intervals are presented in Tables F.2 and F.3, males and females, respectively.

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187 Appendix F (Continued) Table F.2 Licensing Proportions of Senior Males Ages 55+ years in 2000 Cohort Population Licensed Population Licensed Proportion 55-59 6,553,0946,394,207 97.58% 60-64 5,165,6834,970,258 96.22% 65-69 4,402,8444,182,933 95.01% 70-74 3,904,3213,644,990 93.36% 75-79 3,051,2272,820,136 92.43% 80-84 1,853,7951,656,789 89.37% 85yrs+ 1,239,510957,463 77.25% Total 26,170,47424,626,777 94.10% Source: Office of Highway Policy Information, 2001 & Projec ted Population of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) U.S. Census Bureau, Popul ation Division, Populat ion Projections Branch Table F.3 Licensing Proportions of Senior Females Ages 55+ years in 2000 Cohort Population Licensed Population Licensed Proportion 55-59 7,005,9336,366,285 90.87% 60-64 5,699,0264,944,370 86.76% 65-69 5,131,1114,202,950 81.91% 70-74 4,945,6253,822,570 77.29% 75-79 4,374,1513,091,013 70.67% 80-84 3,130,8731,854,278 59.23% 85yrs+ 3,027,790 1,092,687 36.09% Total 33,314,50925,374,15276.17% Office of Highway Policy Information, 2001 & Projected Popul ation of the United States, by Age and Sex: 2000 to 2050 (Detailed Table) U.S. Census Bureau, Popul ation Division, Populat ion Projections Branch Following the same methodology as presented in the main report to determine future cohorts of licensed persons, seniors reaching 85 years and older in 2030, would have been 55 years and older in 2000. Tables F.4 and F.5 present estimated numbers of licensed seniors (85+ years) for the year 2030. Table F.4 Licensing Proportions of Senior Males Ages 85+ years in 2030 Cohort 2000 Licensed Prop' 2000Cohort 2030 Population Licensed Population 55-59 97.58%85-892,044,641 1,995,066 60-64 96.22%90-94884,129 850,681 65-69 95.01%95-99316,977 301,145 70-74 93.36%100+94,190 87,934 Total 3,339,937 3,234,826

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188 Appendix F (Continued) Table F.5 Licensing Proportions of Senior Females Ages 85+ years in 2030 Cohort 2000 Licensed 2000 Cohort 2030 Population Licensed Population 55-59 90.87%85-893,405,952 3,094,986 60-64 86.76%90-941,767,244 1,533,228 65-69 81.91%95-99784,822 642,856 70-74 77.29%100+305,079 235,801 Total 6,263,097 5,506,872 As can be seen in Tables F.4 and F.5, the senior population figures remain the same but the difference is seen in the licensure numbers. When all persons ages 55+ years were grouped together, the estimated number of licensed approximated 3,142,927 males and 4,770,317 females representing 94.1% and 76.2% of the male and female populations in 2030. Disaggregating the cohort of persons ages 55+ years, the resulting numbers of licensed increases to 3,234,826 males and 5,506,872 females respectively. The largest difference between the aggregated and disaggregated licensed populations is seen in the number of senior females licensed, a 15 percent increase, compared to 3 percent for males. An important caveat needs to be noted in the interpretation of the licensed persons ages 85+ years, as stated earlier (see section 3.11), it is assumed that nonlicensed immigrants coming to the U.S. over the next few decades will acquire licensing status similar to that of their respective age cohorts. However, such licensing behavior may be plausible for persons 35 to 55 years during the intervening period, but those persons of older years less so. A person of 55 years and never driven, who immigrated to the U.S. post-2000 and still alive in 2030 is less likely to learn to drive in their senior years, moreso, if they immigrated to join family members who are able to meet the immigrant’s transportation needs. Thus, the revised figures may be an overestimation (i.e., worse case scenario), only time will tell.

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189 Appendix G Population Estimates (2001) Derived from the NHTS Person and Household Files Table G.1 NHTS Population Estimates (2001) Cohort # of Persons (Person File) # of Persons (Household File) Refused 4,5764,572 Don't Know 1,655,4831,409,219 Not Ascertained 2,776,5452,494,438 0 4yrs 19,626,32219,367,504 5 9yrs 20,180,73520,253,848 11 14yrs 20,964,03620,369,433 15 19yrs 18,135,66717,199,613 20 24yrs 16,851,86615,063,338 25 29yrs 18,637,29816,471,331 30 34yrs 22,190,86419,052,234 35 39yrs 20,858,38519,978,465 40 44yrs 22,723,87721,256,029 45 49yrs 18,236,63420,698,123 50 54yrs 17,349,01518,958,851 55 59yrs 13,091,63013,974,639 60 64yrs 11,036,23411,649,376 64 69yrs 9,595,85010,193,810 70 74yrs 8,917,8739,500,593 75 79yrs 7,048,6677,626,033 80 84yrs 4,419,0244,951,975 85yrs+ 2,902,6543,295,199 Total 277,203,235274,828,376 Persons 65 years 32,884,06935,567,610 % Persons 65 years 11.86%12.94%

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190 Appendix H Transit Market Share Assessments – Detailed Calculations Table H.1 Market Assessment #1 LineMartket Analysis #1 All Seniors 12001 Total Trips 0 64yrs366,272,055,294 22001 Total Trips 65yrs+40,990,429,913 32001 Transit Trips 0 64yrs6,149,312,016 42001 Transit Trips 65yrs+503,068,683 52001 0 64yr pop244,319,167 62001 65yrs+ pop32,884,068 72001 Senior Transit %7.56% 8Daily Trip Rate 0 64yrs4.11 9Daily Trip Rate 65yrs+3.42 10Daily Transit Trip Rate 65yrs+0.07 11Daily Transit Trip Rate 0 64yrs0.04 122030 Total Trips 0 64yrs437,949,301,366 132030 Total Trips 65yrs89,067,705,829 142030 Transit Trips 0 64yrs7,352,695,523 152030 Transit Trips 65yrs+1,093,113,040 162030 0 64yr pop292,130,964 172030 65yrs+ pop71,453,471 182030 Senior Transit %12.94% LineExplanation 1Total number of trips 0 64yrs (NHTS 2001) 2Total number of trips 65yrs+ (NHTS 2001) 3Total number of transit trips 0 64yrs (NHTS 2001) 4Total number of transit trips 65yrs+ (NHTS 2001) 50 64yr population (NHTS 2001) 665yrs+ population (NHTS 2001) 7Senior transit trip market share (line 4 / (line 3 + line 4) 8Daily trip rate 0 64yrs 9Daily trip rate 65yrs+ 10Daily transit trip rate 0 64yrs 11Daily transit trip rate 65yrs+ 12Estimated trips (0 64yrs) 2030 = (line 16 line 8 365) 13Estimated trips (65yrs+) 2030 = (line 17 line 9 365) 14Estimated transit trips (0 64yrs) 2030 = (line 16 line 10 365) 15Estimated transit trips (65yrs+) 2030 = (line 17 line 11 365) 16Estimated population ages 0 64 years (census) 17Estimated population ages 65 years+ (census) 18Senior transit trip market share (line 15 / (line 14 + line 15)

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191 Appendix H (Continued) Table H.2 Market Assessment #2 Line Market Assessment #2 Seniors Urban/ Rural UrbanRuralTotal 12001 Total Trips 0 64yrs289,645,261,20176,626,794,127366,272,055,328 22001 Total Trips 65yrs+32,434,626,4858,555,803,42540,990,429,910 32001 Transit Trips 0 64yrs6,018,647,645130,664,3726,149,312,017 42001 Transit Trips 65yrs+500,341,6852,726,998503,068,683 52001 0 64yr pop190,950,30853,368,861244,319,169 62001 65yrs+ pop25,622,4997,261,57132,884,070 72001 Senior Transit %7.68%2.04%7.56% 8Daily Trip Rate 0 64yrs4.163.934.11 9Daily Trip Rate 65yrs+3.473.233.42 10Daily Transit Trip Rate 65yrs+0.090.010.07 11Daily Transit Trip Rate 0 64yrs0.050.000.04 122030 Total Trips 0 64yrs341,204,210,13996,471,304,201437,675,514,340 132030 Total Trips 65yrs69,646,848,85219,363,389,26489,010,238,116 142030 Transit Trips 0 64yrs7,090,010,405164,503,3247,254,513,730 152030 Transit Trips 65yrs+1,074,383,3216,171,7081,080,555,029 162030 0 64yr pop224,940,84267,190,122292,130,964 172030 65yrs+ pop55,019,17316,434,29871,453,471 182030 Senior Transit %13.16%3.62%12.96% Urban/Rural split: 77% and 23% Line explanation see Table H.1

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192 Appendix H (Continued) Table H.3 Market Assessment #3 Line Market Assessment #3 Urban Seniors and Driving Status Active DriverNon & Former DriverTotal 12001 Total Trips 0 64yrs213,313,223,56176,332,037,616289,645,261,177 22001 Total Trips 65yrs+29,216,362,7813,218,263,71132,434,626,492 32001 Transit Trips 0 64yrs2,935,343,7433,083,303,9226,018,647,665 42001 Transit Trips 65yrs+159,513,500340,828,189500,341,689 52001 0 64yr pop127,113,55063,836,759190,950,309 62001 65yrs+ pop19,892,9255,729,57525,622,500 72001 Senior Transit %5.15%9.95%7.68% 8Daily Trip Rate 0 64yrs4.603.284.16 9Daily Trip Rate 65yrs+4.021.543.47 10Daily Transit Trip Rate 65yrs+0.060.130.09 11Daily Transit Trip Rate 0 64yrs0.020.160.05 122030 Total Trips 0 64yrs242,216,674,50496,380,904,381338,597,578,885 132030 Total Trips 65yrs68,191,259,2894,824,328,92573,015,588,215 142030 Transit Trips 0 64yrs3,333,076,0663,893,144,0827,226,220,147 152030 Transit Trips 65yrs+372,305,975510,917,513883,223,488 162030 0 64yr pop144,337,14480,603,699224,940,842 172030 65yrs+ pop46,430,2708,588,90355,019,173 182030 Senior Transit %10.05%11.60%10.89% Line explanation see Table H.1

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193 Appendix H (Continued) Table H.4 Market Assessment #3 Senior Active, Former and Non-Drivers Cohort GenderMenWomenMenWomenMenWomen Population (Year 2030)9,473,10410,507,1588,280,8249,686,8476,159,6577,829,249 Licensing Rate0.9420.9200.9470.9260.9620.932 # Licensed8,922,7179,670,7887,841,9408,971,9585,926,2067,299,209 # Non Licensed550,387836,370438,884714,889233,451530,040 Cessation Rate (Foley et al. 2002)0.000.000.030.060.060.11 Estimate # Former Drivers00227,416529,346367,425802,913 Total # Non Drivers550,387836,370666,3001,244,235600,8761,332,953 Urban 2030 (0.77 Totals) GenderMenWomenMenWomenMenWomen Population 7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Active drivers6,870,4927,446,5075,863,1846,500,8114,280,2625,002,148 NonDrivers423,798644,005513,051958,061462,6741,026,374 Total7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Cohort Total 65yrs+ GenderMenWomenMenWomen Population (Year 2030)4,089,1945,824,4043,339,9376,263,09771,453,471 Licensing Rate0.9700.9270.9410.762 # Licensed3,968,1545,398,6403,142,8814,770,60165,913,093 # Non Licensed121,040425,764197,0561,492,4965,540,378 Cessation Rate (Foley et al. 2002)0.110.190.220.32 Estimate # Former Drivers444,4331,041,938688,2911,512,2815,614,042 Total # Non Drivers565,4731,467,701885,3473,004,77711,154,419 Urban 2030 (0.77 Totals) Total 65yrs+ GenderMenWomenMenWomen Population 3,148,6794,484,7912,571,7514,822,58555,019,173 Active drivers2,713,2653,354,6611,890,0342,508,90746,430,270 NonDrivers435,4141,130,130681,7172,313,6788,588,903 Total3,148,6794,484,7912,571,7514,822,58555,019,173 80-84yrs85yrs+ 65-69yrs70-74yrs75-79yrs 80-84yrs85yrs+ 65-69yrs70-74yrs75-79yrs 193

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194 Appendix H (Continued) Table H.5 Market Assessment #3 2030 Population Estimates Population Estimates A c ti ve D r i vers (@ 85%)** N ever D r i ven (@ 15%)Former Drivers T o t a l N onDriversTotal 0 14 Population 2030*71,600,569 Urban55,132,43855,132,438055,132,43855,132,438 Rural16,468,131nananana 15-64 Population 2030220,530,395 Urban (77 percent)169,808,404144,337,14425,471,261025,471,261169,808,404 Rural (23 percent)50,721,991nananana Senior Population 65yrs+71,453,471 Urban (77 percent)55,019,17346,430,2704,266,0914,322,8128,588,90355,019,173 Rural (23 percent)16,434,298nananana Total Population363,584,435190,767,41384,869,7904,322,81289,192,602279,960,015 Total Population (Urban) 279,960,015 persons *Driving Age 15yrs **Senior active and former drivers as in Table H.4 194

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195 Appendix H (Continued) Table H.6 Market Assessment #4 Urban Non-Driving Seniors According to Household Driver Availability Status LineScenario #4 Urban Non-Driving SeniorsZero Driver in HouseholdDriver in HouseholdTotal 12001 Total Trips 0 64yrs5,392,524,66170,939,512,96276,332,037,623 22001 Total Trips 65yrs+1,343,355,8661,874,907,8413,218,263,707 32001 Transit Trips 0 64yrs1,433,165,5761,650,138,3403,083,303,916 42001 Transit Trips 65yrs+257,148,54283,679,644340,828,186 52001 0 64yr pop5,358,80858,477,95663,836,764 62001 65yrs+ pop2,348,8593,380,7175,729,576 72001 Senior Transit %15.21%4.83%9.95% 8Daily Trip Rate 0 64yrs2.763.323.28 9Daily Trip Rate 65yrs+1.571.521.54 10Daily Transit Trip Rate 65yrs+0.730.080.13 11Daily Transit Trip Rate 0 64yrs0.300.070.16 122030 Total Trips 0 64yrs4,055,542,14092,891,204,60796,946,746,747 132030 Total Trips 65yrs736,822,8394,048,813,0554,785,635,894 142030 Transit Trips 0 64yrs1,077,837,1452,160,761,0733,238,598,219 152030 Transit Trips 65yrs+141,044,472180,703,941321,748,412 162030 0 64yr pop4,030,18576,573,51480,603,699 172030 65yrs+ pop1,288,3357,300,5678,588,903 182030 Senior Transit %11.57%7.72%9.04% Line explanation see Table H.1

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196 Appendix H (Continued) Table H.7 Market Assessment #4 2030 Population Estimates Population EstimatesNever Driven (@ 15%)Former DriversTotal Non-DriversZero Driver in HHDriver in HH 0 14 Population 2030*71,600,569 Urban55,132,43855,132,438055,132,4382,756,62252,375,816 Rural16,468,131nanana 15-64 Population 2030220,530,395 Urban (77 percent)169,808,40425,471,261025,471,2611,273,56324,197,698 Rural (23 percent)50,721,991nanana Senior Population 65yrs+***71,453,471 Urban (77 percent)55,019,1734,266,0914,322,8128,588,9031,288,3357,300,567 Rural (23 percent)16,434,298nanana Total Population363,584,43584,869,7904,322,81289,192,602 Total Population (Urban)279,960,015 *Driving Age 15yrsHousehold SplitZero Driver in HHDriver in HH **Active and former drivers as in Table H.42001 0 64yr pop5.0%95.0% 2001 65yrs+ pop15.0%85.0% 196

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197 Appendix H (Continued) Table H.8 Market Assessment #5 Urban Non-Driving Seniors According to Household Vehicle Availability Status LineScenario #5 Urban Non-Driving SeniorsZero Vehicle in HouseholdVehicle in HouseholdTotal 12001 Total Trips 0 64yrs7,658,773,57768,673,264,04976,332,037,626 22001 Total Trips 65yrs+1,353,076,7561,865,186,9483,218,263,704 32001 Transit Trips 0 64yrs1,801,485,5761,281,818,3443,083,303,920 42001 Transit Trips 65yrs+284,969,78955,858,397340,828,186 52001 0 64yr pop7,436,02656,400,73363,836,759 62001 65yrs+ pop2,376,6093,352,9625,729,571 72001 Senior Transit %13.66%4.18%9.95% 8Daily Trip Rate 0 64yrs2.823.343.28 9Daily Trip Rate 65yrs+1.561.521.54 10Daily Transit Trip Rate 65yrs+0.660.060.13 11Daily Transit Trip Rate 0 64yrs0.330.050.16 122030 Total Trips 0 64yrs8,301,819,79288,328,411,95596,630,231,747 132030 Total Trips 65yrs977,985,4263,822,270,4624,800,255,888 142030 Transit Trips 0 64yrs1,952,741,9711,648,690,8013,601,432,772 152030 Transit Trips 65yrs+205,972,277114,468,901320,441,178 162030 0 64yr pop8,060,37072,543,32980,603,699 172030 65yrs+ pop1,717,7816,871,1228,588,903 182030 Senior Transit %9.54%6.49%8.17% Line explanation see Table H.1

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198 Appendix H (Continued) Table H.9 Market Assessment #5 2030 Population Estimates Population EstimatesNever Driven (@ 15%)Former DriversTotal Non-Drivers Zero Vehilce in Household Vehicle in Household 0 14 Population 2030*71,600,569 Urban55,132,43855,132,438055,132,43800 Rural16,468,131nanana 15-64 Population 2030220,530,395 Urban (77 percent)169,808,40425,471,261025,471,26100 Rural (23 percent)50,721,991nanana Senior Population 65yrs+***71,453,471 Urban (77 percent)55,019,1734,266,0914,322,8128,588,9031,717,7816,871,122 Rural (23 percent)16,434,298nanana Total Population363,584,43584,869,7904,322,81289,192,602 Total Population (Urban)279,960,015 *Driving Age 15yrsHousehold SplitZero Vehicle in HHVehicle in HH **Active and former drivers as in Table H.42001 0 64yr pop10.00%90.00% 2001 65yrs+ pop20.00%80.00% 198

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199 Appendix J Transit Market Share Assessments Sensitivity Tests Table J.1 Market Assessment #4 (Sensitivity Test #1 – Trip Rates Male & Female Licensing Proportions Equal) LineScenario #4 Urban Non-Driving SeniorsZero Driver in HHDriver in HHTotal 12001 Total Trips 0 64yrs5,392,524,66170,939,512,96276,332,037,623 22001 Total Trips 65yrs+1,343,355,8661,874,907,8413,218,263,707 32001 Transit Trips 0 64yrs1,433,165,5761,650,138,3403,083,303,916 42001 Transit Trips 65yrs+257,148,54283,679,644340,828,186 52001 0 64yr pop5,358,80858,477,95663,836,764 62001 65yrs+ pop2,348,8593,380,7175,729,576 72001 Senior Transit %15.21%4.83%9.95% 8Daily Trip Rate 0 64yrs2.763.323.28 9Daily Trip Rate 65yrs+1.571.521.54 10Daily Transit Trip Rate 65yrs+0.730.080.13 11Daily Transit Trip Rate 0 64yrs0.300.070.16 122030 Total Trips 0 64yrs4,055,542,14092,891,204,60796,946,746,747 132030 Total Trips 65yrs631,488,7783,470,006,4564,101,495,234 142030 Transit Trips 0 64yrs1,077,837,1452,160,761,0733,238,598,219 152030 Transit Trips 65yrs+120,881,162154,871,028275,752,190 162030 0 64yr pop4,030,18576,573,51480,603,699 172030 65yrs+ pop1,104,1596,256,8997,361,058 182030 Senior Transit %10.08%6.69%7.85% Line explanation see Table H.1

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200 Appendix J (Continued) Table J.2 Market Assessment #4 (Sensitivity Test #1– Male & Female Licensing Proportions Equal) Cohort GenderMenWomenMenWomenMenWomen Population9,473,10410,507,1588,280,8249,686,8476,159,6577,829,249 Licensing Rate0.9420.9420.9470.9470.9620.962 # Licensed8,922,7179,896,6927,841,9409,173,4445,926,2067,532,520 # Non Licensed550,387610,466438,884513,403233,451296,729 Cessation Rate0.000.000.030.060.060.11 Estimate # Former Drivers00227,416541,233367,425828,577 Total # Non Drivers550,387610,466666,3001,054,636600,8761,125,306 Urban 2030 (0.77 Totals) GenderMenWomenMenWomenMenWomen Population 7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Active drivers6,870,4927,620,4535,863,1846,646,8024,280,2625,162,036 NonDrivers423,798470,059513,051812,070462,674866,485 Total7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Cohort Total 65yrs+ GenderMenWomenMenWomen Population4,089,1945,824,4043,339,9376,263,09771,453,471 Licensing Rate0.9700.9700.9410.941 # Licensed3,968,1545,652,0023,142,8815,893,57467,950,130 # Non Licensed121,040172,402197,056369,5233,503,341 Cessation Rate0.110.190.220.32 Estimate # Former Drivers444,4331,090,836688,2911,868,2636,056,475 Total # Non Drivers565,4731,263,239885,3472,237,7869,559,816 Urban 2030 (0.77 Totals) GenderMenWomenMenWomen Population 3,148,6794,484,7912,571,7514,822,58555,019,173 Active drivers2,713,2653,512,0971,890,0343,099,49047,658,115 NonDrivers435,414972,694681,7171,723,0957,361,058 Total3,148,6794,484,7912,571,7514,822,58555,019,173 75-79yrs 80-84yrs85yrs+ 65-69yrs70-74yrs 200

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201 Appendix J (Continued) Table J.3 Market Assessment #4 Population Estimates (Sensitivity Test #1 – Drivers Male & Female Licensing Proportions Equal) Population EstimatesNever Driven (@ 15%)Former DriversTotal Non-DriversZero Driver in HHDriver in HH 0 14 Population 2030*71,600,569 Urban55,132,43855,132,438055,132,4382,756,62252,375,816 Rural16,468,131nanana 15-64 Population 2030 220,530,395 Urban (77 percent)169,808,40425,471,261025,471,2611,273,56324,197,698 Rural (23 percent)50,721,991nanana Senior Population 65yrs+*** 71,453,471 Urban (77 percent)55,019,1732,697,5724,663,4867,361,0581,104,1596,256,899 Rural (23 percent)16,434,298nanana Total Population 363,584,43583,301,2714,663,48687,964,757 Total Population (Urban)279,960,015 *Driving Age 15yrsHousehold Split Zero Driver in HHDriver in HH **Active and former drivers as in Table J.22001 0 64yr pop5.00%95.00% 2001 65yrs+ pop15.00%85.00% 201

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202 Appendix J (Continued) Table J.4 Market Assessment #4 (Sensitivity Test #2 – Trip Rates Male & Female Cessation Rates Equal) LineScenario #4 Urban Non-Driving SeniorsZero Driver in HHDriver in HHTotal 12001 Total Trips 0 64yrs5,392,524,66170,939,512,96276,332,037,623 22001 Total Trips 65yrs+1,343,355,8661,874,907,8413,218,263,707 32001 Transit Trips 0 64yrs1,433,165,5761,650,138,3403,083,303,916 42001 Transit Trips 65yrs+257,148,54283,679,644340,828,186 52001 0 64yr pop5,358,80858,477,95663,836,764 62001 65yrs+ pop2,348,8593,380,7175,729,576 72001 Senior Transit %15.21%4.83%9.95% 8Daily Trip Rate 0 64yrs2.763.323.28 9Daily Trip Rate 65yrs+1.571.521.54 10Daily Transit Trip Rate 65yrs+0.730.080.13 11Daily Transit Trip Rate 0 64yrs0.300.070.16 122030 Total Trips 0 64yrs4,055,542,14092,891,204,60796,946,746,747 132030 Total Trips 65yrs636,130,8393,495,514,4034,131,645,242 142030 Transit Trips 0 64yrs1,077,837,1452,160,761,0733,238,598,219 152030 Transit Trips 65yrs+121,769,757156,009,482277,779,239 162030 0 64yr pop4,030,18576,573,51480,603,699 172030 65yrs+ pop1,112,2756,302,8947,415,169 182030 Senior Transit %10.15%6.73%7.90% *Driving Age 15yrs **Active and former drivers as in Table J.2

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203 Appendix J (Continued) Table J.5 Market Assessment #4 (Sensitivity Test #2 – Male & Female Cessation Rates Equal) Cohort GenderMenWomenMenWomenMenWomen Population9,473,10410,507,1588,280,8249,686,8476,159,6577,829,249 Licensing Rate0.9420.9200.9470.9260.9620.932 # Licensed8,922,7179,670,7887,841,9408,971,9585,926,2067,299,209 # Non Licensed550,387836,370438,884714,889233,451530,040 Cessation Rate0.000.000.030.030.060.06 Estimate # Former Drivers00227,416260,187367,425452,551 Total # Non Drivers550,387836,370666,300975,076600,876982,591 Urban 2030 (0.77 Totals) GenderMenWomenMenWomenMenWomen Population 7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Active drivers6,870,4927,446,5075,863,1846,708,0644,280,2625,271,927 NonDrivers423,798644,005513,051750,809462,674756,595 Total7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Cohort Total 65yrs+ GenderMenWomenMenWomen Population4,089,1945,824,4043,339,9376,263,09719,516,632 Licensing Rate0.9700.9270.9410.762 # Licensed3,968,1545,398,6403,142,8814,770,60117,280,276 # Non Licensed121,040425,764197,0561,492,4962,236,356 Cessation Rate0.110.110.220.22 Estimate # Former Drivers444,433604,648688,2911,044,7622,782,133 Total # Non Drivers565,4731,030,412885,3472,537,2585,018,490 Urban 2030 (0.77 Totals) GenderMenWomenMenWomen Population 3,148,6794,484,7912,571,7514,822,58515,027,807 Active drivers2,713,2653,691,3741,890,0342,868,89611,163,570 NonDrivers435,414793,417681,7171,953,6883,864,237 Total3,148,6794,484,7912,571,7514,822,58515,027,807 75-79yrs 80-84yrs85yrs+ 65-69yrs70-74yrs 203

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204 Appendix J (Continued) Table J.6 Market Assessment #4 (Sensitivity Test #2 – Drivers Male & Female Cessation Rates Equal) Population EstimatesNever Driven (@ 15%)Former DriversTotal Non-Drivers Zero Driver in Household Driver in Household 0 14 Population 2030*71,600,569 Urban55,132,43855,132,438055,132,4382,756,62252,375,816 Rural16,468,131nanana 15-64 Population 2030220,530,395 Urban (77 percent)169,808,40425,471,261025,471,2611,273,56324,197,698 Rural (23 percent)50,721,991nanana Senior Population 65yrs+***71,453,471 Urban (77 percent)55,019,1734,266,0913,149,0787,415,1691,112,2756,302,894 Rural (23 percent)16,434,298nanana Total Population363,584,43584,869,7903,149,07888,018,868 Total Population (Urban)279,960,015 *Driving Age 15yrsHousehold SplitZero Driver in HHDriver in HH **Active and former drivers as in Table J.52001 0 64yr pop5.00%95.00% 2001 65yrs+ pop15.00%85.00% 204

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205 Appendix J (Continued) Table J.7 Market Assessment #4 (Sensitivity Test #3 – Trip Rates Male & Female Licensing and Cessation Rates Equal) LineScenario #4 Urban Non-Driving SeniorsZero Driver in HHDriver in HHTotal 12001 Total Trips 0 64yrs5,392,524,66170,939,512,96276,332,037,623 22001 Total Trips 65yrs+1,343,355,8661,874,907,8413,218,263,707 32001 Transit Trips 0 64yrs1,433,165,5761,650,138,3403,083,303,916 42001 Transit Trips 65yrs+257,148,54283,679,644340,828,186 52001 0 64yr pop5,358,80858,477,95663,836,764 62001 65yrs+ pop2,348,8593,380,7175,729,576 72001 Senior Transit %15.21%4.83%9.95% 8Daily Trip Rate 0 64yrs2.763.323.28 9Daily Trip Rate 65yrs+1.571.521.54 10Daily Transit Trip Rate 65yrs+0.730.080.13 11Daily Transit Trip Rate 0 64yrs0.300.070.16 122030 Total Trips 0 64yrs4,055,542,14092,891,204,60796,946,746,747 132030 Total Trips 65yrs631,488,7783,470,006,4564,101,495,234 142030 Transit Trips 0 64yrs1,077,837,1452,160,761,0733,238,598,219 152030 Transit Trips 65yrs+120,881,162154,871,028275,752,190 162030 0 64yr pop4,030,18576,573,51480,603,699 172030 65yrs+ pop1,104,1596,256,8997,361,058 182030 Senior Transit %10.08%6.69%7.85% Line explanation see Table H.1

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206 Appendix J (Continued) Table J.8 Market Assessment #4 (Sensitivity Test #3 Male & Female Licensing and Cessation Rates Equal) Cohort GenderMenWomenMenWomenMenWomen Population9,473,10410,507,1588,280,8249,686,8476,159,6577,829,249 Licensing Rate0.9420.9420.9470.9470.9620.962 # Licensed8,922,7179,896,6927,841,9409,173,4445,926,2067,532,520 # Non Licensed550,387610,466438,884513,403233,451296,729 Cessation Rate0.000.000.030.030.060.06 Estimate # Former Drivers00227,416266,030367,425467,016 Total # Non Drivers550,387610,466666,300779,433600,876763,745 Urban 2030 (0.77 Totals) GenderMenWomenMenWomenMenWomen Population 7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Active drivers6,870,4927,620,4535,863,1846,858,7094,280,2625,440,438 NonDrivers423,798470,059513,051600,163462,674588,084 Total7,294,2908,090,5126,376,2347,458,8724,742,9366,028,522 Cohort Total 65yrs+ GenderMenWomenMenWomen Population4,089,1945,824,4043,339,9376,263,09719,516,632 Licensing Rate0.9700.9700.9410.941 # Licensed3,968,1545,652,0023,142,8815,893,57418,656,610 # Non Licensed121,040172,402197,056369,523860,022 Cessation Rate0.110.110.220.22 Estimate # Former Drivers444,433633,024688,2911,290,6933,056,441 Total # Non Drivers565,473805,427885,3471,660,2153,916,463 Urban 2030 (0.77 Totals) GenderMenWomenMenWomen Population 3,148,6794,484,7912,571,7514,822,58515,027,807 Active drivers2,713,2653,864,6131,890,0343,544,21912,012,130 NonDrivers435,414620,178681,7171,278,3663,015,676 Total3,148,6794,484,7912,571,7514,822,58515,027,807 75-79yrs 80-84yrs85yrs+ 65-69yrs70-74yrs 206

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207 Appendix J (Continued) Table J.9 Market Assessment #4 (Sensitivity Test #3 Drivers Male & Female Licensing and Cessation Rates Equal) Population EstimatesNever Driven (@ 15%)Former DriversTotal Non-Drivers Z ero D r i ver i n Household D r i ver i n Household 0 14 Population 2030*71,600,569 Urban55,132,43855,132,438055,132,4382,756,62252,375,816 Rural16,468,131nanana 15-64 Population 2030220,530,395 Urban (77 percent)169,808,40425,471,261025,471,2611,273,56324,197,698 Rural (23 percent)50,721,991nanana Senior Population 65yrs+***71,453,471 Urban (77 percent)55,019,1732,697,5723,375,9336,073,505911,0265,162,479 Rural (23 percent)16,434,298nanana Total Population363,584,43583,301,2713,375,93386,677,204 Total Population (Urban)279,960,015 *Driving Age 15yrsHousehold SplitZero Driver in HHDriver in HH **Active and former drivers as in Table J.82001 0 64yr pop5.00%95.00% 2001 65yrs+ pop15.00%85.00% 207

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208 Appendix K Focus Group Questionnaire – Current Drivers TRANSIT USE VIABILITY OF SENIORS LOSING DRIVING PRIVILEGES (Persons who have reduced their driving exposure) This study is about senior travel behavior, how do you get from home to the grocery store, or pharmacy and back home again? We will ask you questions about how you travel locally. We don’t require your nam e and will not be selling you anything and your responses will remain confidential. Questions about your driving status 1. How many years have you driven to date? more than 40 years 21 to 40 years 6 to 20 years 1 to 5 years 2. Do you hold a valid driver’s lic ense issued by any state in the U.S.? Yes (go to question 3) No (go to question 4) 3. In which year will your cu rrent driver’s license expire? 2006 2007 to 2008 2009 to 2010 2011 or later 4. Are you currently driving a ca r for at least one trip per week? Yes No Questions about transportation 5. Which transportation mode do you currently use for the majority of your local trips? (check one response only) Drive myself (in a personally operated vehicle) Car passenger (where someone else is driving) Public transit (i.e., Hartline Bus, Sunshine Line, Trolley) Other ………………………………. 6. What one factor gives you concern about using public transit? (check one response only) Being worried about the expense of using public transit Public transit does not go where I want to go Public transit takes too long to get to where I want to go Getting to and traveling on public transit is difficult Public transit is not available when I need to travel Being worried about crime on public transit Lack of information about public transit

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209 Appendix K (Continued) 7. What one factor if changed would make public transit an option for you to use today? (check one response only) Free or low cost public transit services Many more destinations (i.e., it goes to where I want to go) Faster service (i.e., takes a short time to where I want to go) Accessibility (i.e., closer to my home and easier to get on or off) Higher frequency of services Visible personal safety and security measures, e.g. transit police More information and training about using public transit Nothing – I still would not use public transit despite improvements Questions about you and your household 8. Total number of persons in your household (including yourself)? ........ 9. Total number of persons with driver’s licenses in your household (including yourself)? ........ 10. Total number of vehicles in your household ........ 11. Who normally drives the car in your household? Yourself Spouse/partner/significant other Someone else (other than spouse/partner/significant other) Not driven at all 12. Who would be your first choice in assisting you with transportation if you needed it? (check one only) Spouse/significant other Adult children Other relative (e.g., son-in-law or grandchild related to you) Friend/neighbor/volunteer (unrelated to you) Caretaker/Hired-help (not a taxi) No one else Questions about You 13. Gender Male Female 14. Are you? Between 55 years and 64 years of age Between 65 years and 74 years of age Between 75 years and 84 years of age Above 85 years of age

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210 Appendix K (Continued) 15. What is your race or ethnic heritage? White non-Hispanic African American/Black non-Hispanic Hispanic of any Race Other 16. Which of the following best describes your annual household income in 2005? Up to $30,000 Over $30,000 17. If you had to relocate to another resi dential location this year how important would access to public transit be to you? (check one response only) Extremely important Important Somewhat important Unimportant Irrelevant/no importance 18. In the past 12 months have you considered that you may have to stop driving at some time in the future? Yes No 19. Do you think that public transit (i.e., Hartline Bus, Sunshine Line, Trolley) is a viable transportation alternative for you to use today? Yes No There is a possibility that we would like to follow up later this year on your travel experiences, would this be OK with you? Yes No Name …………………………………………… Contact Number ……………………………………. Thank you!!!

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211 Appendix L Focus Group Questionnaire – Former and Non-Drivers TRANSIT USE VIABILITY OF SENIORS LOSING DRIVING PRIVILEGES (Persons who have permanently stopped driving) This study is about senior travel behavior, how do you get from home to the grocery store, or pharmacy and back home again? We will ask you questions about how you travel locally. We don’t require your nam e and will not be selling you anything and your responses will remain confidential. Questions about your driving status 1. How many years had you driven at the time when you stopped? more than 40 years 21 to 40 years 6 to 20 years 1 to 5 years 2. Do you hold a valid driver’s lic ense issued by any state in the U.S.? Yes (go to question 3) No (go to question 4) 3. In which year will your cu rrent driver’s license expire? 2006 2007 to 2008 2009 to 2010 2011 or later Questions about when you permanently stopped driving 4. Which year did you stop driving? 1989 or before between 1990 and 1999 between 2000 and 2002 between 2003 and 2005 5. Which was the primary factor that influenced you to stop driving? (check one response only) License revoked Health reasons Financial reasons Personal discomfort with driving Family pressure Other ……………………………………………

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212 Appendix L (Continued) 6. Since the time that you stopped driving till now have you used public transit (e.g., Hartline Bus, Sunshine Line, Trolley) for any local trip, i.e., from your home to the grocery store or doctor? Yes No Questions about transportation 7. Which transportation mode do you currently use for the majority of your local trips? (check one response only) Car passenger (where someone else is driving) Public transit (i.e., Hartline Bus, Sunshine Line, Trolley) Walk Other ………………………………. 8. What one factor gives you concern about using public transit? (check one response only) Being worried about the expense of using public transit Public transit does not go where I want to go Public transit takes too long to get to where I want to go Getting to and traveling on public transit is difficult Public transit is not available when I need to travel Being worried about crime on public transit Lack of information about public transit 9. What one factor if changed would make public transit an option for you to use today? (check one response only) Free or low cost public transit services Many more destinations (i.e., it goes to where I want to go) Faster service (i.e., takes a short time to where I want to go) Accessibility (i.e., closer to my home and easier to get on or off) Higher frequency of services Visible personal safety and security measures, e.g. transit police More information and training about using public transit Nothing – I still would not use public transit despite improvements Questions about you and your household 10. Total number of persons in your household (including yourself)? ........ 11. Total number of persons with driver’s licenses in your household (including yourself)? ........ 12. Total number of vehicles in your household

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213 Appendix L (Continued) 13. Who normally drives the car (the majority of the time) in your household? (check one response only) Spouse/partner/significant other Someone else (other than spouse/partner/significant other) Not driven at all A car is not available in my household to drive 14. Who would be your first choice in assisting you with transportation if you needed it? (check one response only) Spouse/significant other Adult children Other relative (e.g., son-in-law or grandchild related to you) Friend/neighbor/volunteer (unrelated to you) Caretaker/Hired-help (not a taxi) No one else Questions about You 15. Gender Male Female 16. Are you? Between 55 years and 64 years of age Between 65 years and 74 years of age Between 75 years and 84 years of age Above 85 years of age 17. What is your race or ethnic heritage? White non-Hispanic African American/Black non-Hispanic Hispanic of any Race Other 18. Which of the following best describes your annual household income in 2005? Up to $30,000 Over $30,000 19. If you had to relocate to another residentia l location this year how important would access to public transit be to you? (check one response only) Extremely important Important Somewhat important Unimportant Irrelevant/no importance

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214 Appendix L (Continued) 20. Do you think that public transit (e.g., Hartline Bus, Sunshine Line, Trolley) is a viable transportation alternative for you to use today? Yes No There is a possibility that we would like to follow up later this year on your travel experiences, would this be OK with you? Yes No Name …………………………………………… Contact Number ……………………………………. Thank you!!!

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ABOUT THE AUTHOR Oliver A. Page received a Bachelor of Science (Honorsmagna cum laude ) in Maritime Studies (International Transport Option) from the University of Wales (UK) in 1985; a Master of Science in Transportation Planning and Engineering from the University of Southampton (UK) in 1989; and a Certif icate in Logistics Management from Rand Afrikaans University in South Africa in 2000. He has over 15 years of professional and academic experience in transportation planning and engineering, working for private, local/state, and international agencies in Europe, North America and Southern Africa. Throughout his professional and academic ca reers, he has published in peer-reviewed journals, presented at numerous professional meetings, and won several awards. Mr. Page has a passion for encouraging high school students to consider transportation as a ‘hot’ career option.