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The effects of age on the driving habits of the elderly

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Title:
The effects of age on the driving habits of the elderly evidence from the 1990 NPTS
Physical Description:
1 online resource (vii, 43 leaves). : ;
Language:
English
Creator:
Chu, Xuehao
University of South Florida -- Center for Urban Transportation Research
National Urban Transit Institute (U.S.)
Publisher:
University of South Florida, Center for Urban Transportation Research
Available through the National Technical Information Service
Place of Publication:
Tampa, Fla
Springfield, VA
Publication Date:

Subjects

Subjects / Keywords:
Older automobile drivers -- United States   ( lcsh )
Older automobile drivers -- Psychology -- United States   ( lcsh )
Traffic accidents -- United States   ( lcsh )
Genre:
bibliography   ( marcgt )
technical report   ( marcgt )
non-fiction   ( marcgt )

Notes

Bibliography:
Includes bibliographical references (leaves 40-43).
Funding:
Performed by the National Urban Transit Institute in cooperation with the U.S. Dept. of Transportation under contract no.
Statement of Responsibility:
Xuehao Chu.
General Note:
"October 1994."

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Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
aleph - 029197469
oclc - 754239613
usfldc doi - C01-00029
usfldc handle - c1.29
System ID:
SFS0032152:00001


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.JL National "1Q!I. Urban .JOQL Transit .,.,. .,.,. Institute 01 the CENTR FOR URBAN TRANSPORT AnON RESEARCH Unlve,..ity o t South Florida Florida State University Florida A&M University Florida International Uni versity THE EFFE CTS OF AGE ON THE DRIVING HABIT S O F THE ELDERLY : Evi denc e from the 1990 NPTS Xuehao Chu Principal Investigator October 1994 Center for Urban Transportat i on Research University of South Florida 4202 E. Fowler Avenue, ENB 118 Tampa FL 33620

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TEONCAL REPORT STAICWtD ml PAGE 1 Rtpolt No. NUT193USF3.2 2. Gaerntllltll Aaallian No. 3 4., ,._ ....:! SllbOIM; 5 THE EFFECTS OF AGE ON THE DRIVING HAB I TS OF THE ELDERLY : 8 Perfcrming EVIDE N CE F ROM THE 1990 NPTS 7 At.llhOtt.} 8. Xuehao Chu Research Ass ociate t. Perfotming HM\11 _, MchH 1 0. WOrli: National Urban T ra n s i t I nstitute Cente r for U r ban Transportation Research University of South F l orida ' DTRS 93-G-0019 4 202 E. Fowle r Avenue E N B 118 Tampa Florida 33620-5350 /llqMw:f .nc!Adcnu 13. T ype ot R..port Wid Period Cov.ed Office of Researc h and S p ecia l P r ograms U.S. Department of T ransp o rtation, Wash i ngton D C 20690 14., 15. ... 1Qiy .... Supported by a grant from t h e U S. Department o f Transportat i on Un ivers ity Resea r ch I n stitute Program 16, This report examines the effects of age on the d r iv ing habits of the elderly using the 1990 N atio n wide Persona l Tra n sportation Survey (N P TS). E l derly i s defi ned as pe r so n s 65 years or older. Six aspects a r e considered : t h e amount of dally driving exposure driving by time of day drivi n g speed dri ving by type of roadways, vehic l e size, and the number of pass en gers carried. T h e scope of analysis i s limited to t h e content of the 1991 N PTS and t hose aspects of driv i ng h a b its t h a t are hypo t hes i zed t o h ave safety i m p li cat i ons for the e l derly. The sca l e of a n alys i s is l imited to urban reside nts. Regression is used to iso late t h e effects of being elderly w h ile holding con stant a set of personal h ouseho l d, and loca t ion cha r acterist ics of the dri vers, as w e ll as a set o f trip c h aracterist ics. Elderly drivers show an incr eased effort of self-p r otect ion i n t h e i r driv i n g h abits re lati ve to m i d-aged drivers (perso n s be tw een the ages o f 2 5 and 64 ye a rs). Being e l der l y not onl y makes elderly dri vers reduce dai ly driv i n g exposure avoid driving at n i ght, avo i d d r i ving duri n g p e ak hou r s and avoid driving on lim it ed-access hig h ways but a lso make them drive at lower speeds drive l arger automobiles and ca r ry fewe r pass engers. D e sp ite t h eir effort of seHpro t ect i on, however, the e l derly still s how a h igher risk o f crash a n d i njury per u n it o f ex posure than the mid a g ed If po l icies induce the elderly to furt h er adj ust the i r dri ving habits to offsel the external ri sks o f thei r driving th e i r risk of crash a n d i nj u ry wou l d be red u ced and socie ty as a who l e wou l d be better off T h e e l d e rly how e ve r a r e li k ely to be worse off as a consequence o f reduced mob i l ity. Th e challe n ge to pol i c y -making is to bal ance t hese con s eque n ces o f any pol icy co ncerni n g t h e mob i l ity and t r affic safety of the elderly. .... key WOtiJt ,a_ Di"llri!Mion age effects driving hab it s eld erly Available t o t h e p u b l i c thro u gh t h e N ational T echnica l I n f ormatio n Serv i ce drivers (NTIS), 5285 P ort Royal Road Spr i n gfield VA 22181 p h (703) 4 87-46 5 0 HI, $IICVIf)' Clusif lot 11111 NIPOtl. 20. s.o..ity Ontif. ( (lllf'ile PtOe) :1. Net or P9t 22. PM:t Uncla ssified U n c l assi fied 48 Fonn DOT F 1700 7 (8-&91

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TABLE OF CONTENTS Table o f Contents . . . . . . . . . . . . . . . . . . . . . . . i ii List of Tables ................ .. ............... .. : . . . . . . . . iv Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . v Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . v i Chapter 1 : Introduction . . . . . . . . . . . . . . . 1 Background . . . . . . . . . . . . . . . . . . . . . . . . 1 Issues and Hypotheses . . . . . . . . . . . . . . . . 2 Previous Stud i es . . . . . . . . . . . . . . . . . . . . . 3 Approach and Organization of the Report . . . . . . . . . . . . 5 Chapter 2 : The 1 ggo Nat ionwide Personal Transportation Survey . . . . . 5 S u rvey . . . . . . . . . . . . . . . . . . . . . . . . . 5 Variables . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 3 : The Effects of Age o n How Much the Elderly Drive . . . . 8 Number o f Da ily Vehic l e M iles . . . . . . . . . . . . . . . . . 8 Number o f Daily Veh ic le Tri ps . . . . . . . . . . . . . . . . . 12 Distance of Daily Vehic l e T rips . . . . . . . . . . . . . . . . 15 Chapter 4 : The Effects of Age on When the E lderly Drive . . . . . . . 18 Driv ing at Nig h t . . . . . . . . . . . . . . . . . . . . . 18 Driving During Peak Hours . . . . . . . . . . . . . . . . 2 1 Chapter 5 : The Effects of Age on How the Elderly Drive . . . . . . . . . 24 Speed . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Limited-Access H i ghways . . . . . . . . . . . . . . . 27 Automobile Size . . . . . . . . . . . . . . . . . . . . 30 Number of Passengers Carried . . . . . . . . . . . . . 34 Chapter 6 : Summary and Policy Implications . . . . . . . . . . . . . 37 Summary . . . . . . . . . . . . . . . . . . . . . 37 Policy Im plications . . . . . . . . . . . . . . . . . 38 Endnotes . . . . . . . . . . . . . . . . . . . 40 iii

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LIST OF TABLES T able 2 1 Definition of va ri ables . . . . . . . . . . . . . . . . . 7 Table 3 1 Average number of da il y vehicle m i les by driver age group . . . 8 Table 3 2 Tobit analys i s of d aily vehicle miles . . . . . . . . . . . . . 10 Table 3 3 Average number of dai l y vehicle trips by driver age group . . . . . 12 Table 3.4 Tobit analysis of number of da i ly vehic l e trips . . . . . . . . 14 Table 3 5 Average distance of dai l y vehic l e trips by driver age group . . . . 15 Table 3 6 Weighted regression of distance of dai l y veh i c l e trips . . . . . 17 Table 4 1 Percent of miles driven at nigh t by driver age group . . . . . . 18 Tab l e 4 2 logit analys i s of driving at nig h t . . . . . . . . . . . . . . 20 Table 4 3 Per cent of m il es driven during peak hours by d river age group . . . 21 Tab l e 4.4 logit ana l ysis of driving du ri ng peak hours . . . . . . . . . . 23 T able 5 1 Average speed on all roads by driver age group . . . . . . . . 24 Table 5 2 Average s p eed on limited access highways by drive r age group . . . 25 Table 5.3 Weighte d r egression of speed of daily veh i cle trips . . . . . . . . 26 Table 5.4 Percent of miles driven on l i mited-access highways by driver age group 28 Table 5 5 Logit analys i s of driving on l im i ted-access highways . . . . . . . 29 Table 5 6 Average size of automobi les by age group of mai n d r ivers . . . . 31 Table 5 7 Weigh t ed r egression of automobi l e size . . . . . . . . . . . 33 Table 5 8 Average occupancy of automobile trips by driver age group . . . 34 Table 5 9 We i ghted regression of occu p ancy of automob i le trips . . . . . . 35 iv

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ACKNOWLEDGMENTS This project is made possible through a grant from the U S Department of Transportation, University Research Inst itute Program. Their support is gratefully acknowledged. Comments from the following individuals are gratefully acknowledged: William L. Ball, Michael R. Baltes, Patricia Henderson, Rosemary Math ias, Steve Polzin, and Joel R. Rey. v

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ABSTRACT The Effects of Age on the Driving Habits of the Elderly : Evidence from the 1990 NPTS This report examines the effects of age on the driving habits of the elderly, using the 1 990 Nationwide Personal Transportation Survey (NPTS) Elderly i s defined as persons 65 years or older. Six aspects are considered : the amount of daily driving exposure, driving by time of day, driv i ng speed driving by type of roadways vehicle size and the number of passengers carried The scope of analysis is limited to the content of the 1991 NPTS and those aspects of driving habits that are hypothesized to have safety implicat i ons for the elderly. The scale of analysis is lim ited to urban res i dents Regression i s used to i solate the effects of being elderly while ho l ding constant a set of personal, household, and location characterist i cs of the drivers as we ll as a set of trip characteristics E l derly drivers show an i ncreased effort of self protection In their driving haMs re l ative to mid-aged drivers (persons between the ages of 25 and 64 years) Be i ng elderly not only makes e l derly drivers reduce daily driving exposure avoid driving at night avoid driving during peak hours and avoid driving on l imited-access highways, but also make them drive at lower speeds, drive larger automobiles and cany fewer passengers Desp ite their effort of self-protection however the elderly still show a h i gher risk of crash and injury per unit of exposure than the mid-aged If policies induce the elderly to further adjust their driving habits to offset the external risks of their driving their risk of crash and injury would be reduced and society as a wh o le w o uld be better off. The elderly however are l i kely t o be worse off as a consequence of reduce d mobility The challenge to po li cy-making i s to balance these consequences of any policy concerning the mobi l ity and traffic safety of the elderly VI

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Chapter 1 INTRODUCTION The mobility and traffic safety of elder ly drivers are of great concern to the public.' Much of this concern is due to the fast growth in the number of elderly drivers and their driving. This report examines the effects of age on the driving habits of the elderly in the States, as revealed in the 1990 Nationw i de Personal Transportation Survey (NPTS)2 Six aspects of driving haMs are cons idered that are hypothesized to have safety implications for the elderly. A good understanding of the driving habits of the elderly i s essential not only to the provision of public transportation to the elderly but also to the design of policies that address the mobility and traffic safety of the elderly BACKGROUND Betwee n 1965 and 1969, three national conferences were held to discuss Issues on the mobility and traffic safety of elderly drivers.3 in 1986 by the Transportation Research Board (TRB), the U.S. Congress requested in the Surface Transportation Assistance Act of 1987 "a comprehensive study and invest i gat i on of (1) problems which may inhibit the safety and mobility of elderly drivers using the Nation's roads and (2) means of addressing these In 1987, Congress asked the U.S. Department of Transportation to implement a pilot program of highway safety improvements to enhance the mobility and traffic safety of elderly drivers In add ition, elderly drivers frequently make headlines in major magazines and newspapers across the nat ion. The number of elderly drive rs grew from 8.6 million in 1970 to 22.3 million in 1990, an increase of 148 percent, while the number of all drivers grew by 50 p ercent during the same period. The number of elderly drivers as a proportion of all drivers also increased from 8.0 percent in 1970 to 13 3 percent in 1990.7 These increases reflect the growth in the elderly population as well as in its licensure rate. The el derly population grew from 20.0 million in 1970 to 31.1 million in 1990, an increase of 56 percent, while the population of age 15 years or older grew by 34 percent during the same pe riod. The licensure rate of the elderly pop ulatio n increased from 45 percent in 1970 to 72 percent in 1990, while the li censure rate of the population of age 15 years or older increased from 77 percent in 1970 to 86 percent in 19909 The number of miles driven by the elderly has grown more than the elderly population and licensure rate. The elderly drove 42.2 billion miles in 1969 and 153.7 billion miles in 1990, an Increase of 264 percent. The rate of growth for all drivers was 142 percent. The share of miles driven by the elderly increased from 4.9 percent in 1969 to 7.1 percent in 1990 .10 These trends are expected to continue. By the year 2020, the elderly population is expected to reach 20 percent of all persons. The number of elderly drivers is likely to exceed 20 percent of all drivers." 1

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ISSUES AND HYPOTHESES Thi s report cons i ders six aspects of driv i ng habits. These aspects include the amount of daily driving exposure, driving by t ime of day, driving speed, driv i ng by type of roadways, vehicle size and the number of passengers carried. The scope of analysis is l im i ted to the content of the 19 9 0 NPTS and t o those aspects of driving haMs that are hypothesized to have safety implications for the elderly. The scale of analysis is limited to urban residen t s. In addit i on to age other personal household and l ocation characteristics of the elderly also may influence their driving habits Personal characteristics include educational attainment and labor force participation. Household characteristics inc l ude race, annual income composition (size children), an d vehicle ownership Location characteristics inc l ude the household location in an urban area (central city vs. suburbs), the household location in the nation (the West vs. other regions), the size of an urban area and the popu l ation density of an urban area. Many of these characteristics may ditfer systematically between the elderly and others. Lab o r force participation changes with aging. Household income may decline with ret i rement from the labor force. Household composition may change with aging. For example, the elderly are less l ikely to l ive with young children than are younger pers o ns. Vehic l e ownership may change with aging due to changes in household comp o siti o n and i nc o me H o usehold location may change with aging For example, the elderly may be more likely to live in the suburbs and in the South The e l derly have more time avai l ab l e for travel during the day. T he elderly also may differ from others in their activity patterns The elderly may choose to participate in activities that occur less frequent l y (e.g once a month instead of once a week). They may choose to participate in activit ies that are closer to their homes. Or they may move closer to activities in which they choose to participate. They a l so may choose to participate in activities that occur during the day or off-peak hours However, the l iterature prov i des no evidence of these hypothetical changes in the activity pattems of the elderly It is important to control f o r the characteristics that d i ffer systematically between the elderly and others in order to isolate the effects of age on the driv ing habits of the elderly I t is also important to control for these characteristics in order to draw conclusions about the driving habits of the future's e l derly from the driving habits of today s elderly because many of these characteristics may change in the future for the elderly. For example, the future's e l derly may have higher vehic l e ownership than today's elderly The future's elderly also may be more like l y to live in the suburbs than today's elderly. The elderly differ from others in two other important characteristics that have not been discussed. First the majority of the elderly are not emp l oyed and will remain unemployed for the rest of their lives. The elderly, tht;refo; e, would lose less than younger persons in future labor eamings from an injury. According to the foregone-labor-earnings approach to measuring motor vehicle crash cos t s elderly drivers a r e likely to have lower costs of injuries than younger drivers 2

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Second, cognijive and physical abilijies generally decline with aging.13 One consequence of this decline is that the driving skills of the elderiy are reduced. As a result, elderly drivers are more likely to be invo lved in crashes than all drivers except those under the age of 25 years. In the majority of crashes in which elderly drivers were involved, they were at fault for failing to yield the right-of-way, turning improperly, ignoring traffic signals. or starting improperly into traffic." Another consequence of the decline in their physical abilities is that the elderly are more likely to be injured than younger persons in a crash. These two important characteristics of the elderly may have two opposite effects on their driving habits On the one hand elderly drivers may be more willing than younger drivers to take risks because of their reduced costs of injuries. On the other hand, elderly drivers may compensate for their increased crash and injury ris ks. This behavior of risk compensation can manifest ijself in many ways The elderly may drive fewer miles to reduce exposure. They may feel less comfort able with carrying passengers They may find certain driving conditions difficult, such as driving at night, during peak hours, at high speeds, or on limited -access hi ghway s. They also may feel vulnerable to the low crashworthiness of small vehicles. While this study controls for ma ny of the personal household and l ocation characteristics of the elderly discussed earlier, it does not however, control for the two important characteris t ics just discussed. It is hypothesized that the relative strengths of these two characteristics determine the effects of age on the driving haMs of t he elderly. PREVIOUS STUDIES No known previous study exi sts that looks at the size of vehicles that the elderly drive or the number of passengers they carry. Previous stud ie s on the amount of driving exposure, driving speed driving by time-of-day, and driving on lim i ted-access highways by the elderly have one drawback: they often fail to control simullaneously for many factors that may influence the driving habijs of the elderly. This drawback has two imp li cations. On the one hand any observed difference in the driving habits between the elderly and others may be a mix of the differences in age and other personal, househo ld, and loca t ion characteristics of the drivers that are not controlled for in these studies On the other hand, any difference observed in the driving habits of today's elderly and others is unlikely to hold true in the future because those personal, household, and location characteristics of the drivers that are not co nt rolle d for may change in the future The evidence from previous studies is mixed. Studies have found "no evidence that e lderly drivers who exhibit poor performance on driving simulators make any compensat ing adjustment i n the amount of driving exposure." One reason given is that elderly drivers are unaware of the changes in their cognitive and physical abilities and those driving conditions that become more difficult as age advances. The other reason given is that elderly drivers are unwilling to admit lack of driving competence or to significanlly reduce exposure. Several U.S. studies, however. find that elderly drivers reduce exposure more as they age and tend to avoid 3

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high-risk conditions, such as driving at night and during peak hours. '7 A Canadian study concludes that "increased driver risk due to medica l conditi ons among elderly drivers was more than offset by their adoption of new, less risky driving patterns."" APPROACH AND ORGANIZATION OF THE REPORT This study uses re gress ion analysis to isolate the effects of age on the driving habits of the elderly Regression analysis accomplishes this isolation by including variables measuring the age as well as a set of other personal, household and location characteristics of the drivers. I t is imp ortant to control for factors that aging may affect It is also importa nt to control for factors that aging does not affect, such as gender and race. Under this regression framework this study attempts to determine whether or not age affects the driving habits of the e lderly and, if so, what the size and nature of the effects are. This report is organized into six chapters. Chapter 1 is this introd uct ion. Chapter 2 describes the 1990 NPTS and the variables that are used in this study. Chapter 3 examines the effects of age on how much the elderly drive. The aspects examined include the number of daily vehicle miles driven by in div idual drivers, the number of daily vehicle trips taken by individual drivers, and the distance of individual vehicle trips. Chapter 4 examines the effects of age on when the elderly drive The aspects examined include driving at night and during peak hours. Chapter 5 examines the effects of age on how the elderly drive. The aspects examined include driving speed, driving on limited-access highways, vehicle size, and the number of passengers carried. Chapter 6 summarizes the main results and discusses policy implications of these results. 4

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Chapter 2 THE 1990 NATIONWIDE PERSONAL TRANSPORTATION SURVEY (NPTS) This chapter describes the 1990 NPTS and defines the variables that are used in this study. The 1990 NPTS compiles data on a cross-section of personal travel in the United States for all purposes and surface modes of transportation in 1990-1991. SURVEY The 1990 NPTS was conducted between March 1990 and March 1991 using random-digit dialing and computer-assisted telephone interviewing The sample was stratified by geography, quarter-of-year, month-of-quarter, and day-of-week. A total of 73,579 telephone numbers was randomly selected to ident ify 26,172 households. Each of the identified households was contacted for an interview A total of 21 ,669 households participated. Each of the participating households was assigned a 24-hour 'lravel day" and a 14-day "travel period." For each participating household, a household-level interview was conducted with an adult resident of the household. This interview obtained information on the number of household vehicles, household loca t i on, and household in come. In addition, a roster containing person data for each resident of the household was comp le ted A person-level interview was attempted for each resident of the participating households who was five years or older. The person-level interview was completed for 47,499 household residents. Each resident older than 13 years was asked to report all trips they had taken during the travel day, as well as trips of 75 miles or longer taken during the travel period. A "knowledgeable" household resident older than 13 years. was asked to report all trips taken by household residents between the ages of 5 and 13 yea rs The 1990 NPTS data for th is study are contained in fou r files in the Statistical Analysis System (SAS) format. The four files are the Household File Person File, Vehicle File, and Travel Day File. The Household File contains household characteristics for 22,317 observations The information collected includes household race, household income household size, and household location, such as census region. the location in an urbanized area, the size of an urbanized area, and the population density of a zip-code area. Also included are the sunrise and sunset times associated with the travel day. The Person File contains the person-level attributes for 46,365 residents of the participating households. The information collected includes the age educational attainment, driver's license status. and labor force participation of each household resident. Participating in the labor force means being employed or active ly looking for employment. The Person File also contains the number of vehicle miles and the number of vehicle trips taken by each resident on the travel day. 5

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The Vehicle File co ntains the attributes for 41,178 vehicles in the participating households The informat ion collected includes the mode l year make, model, and main driver of each vehicle. The Travel Day File contains the attributes of 149 546 trips taken by residents of the participating households on the travel day. The information collected includes the purpose. mode, occupancy, length (both duration and distance) time-of-day day-of-week, and month-of year of each trip. The survey also randomly selected a private vehicle trip for each resident of the participating households (i f any) to collect information on t he various types of roadways that were used on this trip. A total of 31,015 such trips was sampled The distance for each of these trips was broken down by road way classification Weights were developed in the 1990 NPTS to reflect the sample design and selection probabilities, and survey non-response or The Household and Veh i cle Files have the same weight variable. The Person and Travel Day F il es have separate weight va ri ables. A weight variable was also developed for the randomly selected private-vehicle trips. VARIABLES The variables used in t his study are defined in Table 2.1. They are organized into five groups: personal household location trip and vehicle characteristics 6

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Table 2.1 Defin ition of variables Variabl e Personal Char acteristics Age>-65 Age<4 Hale Educat i on>high school Househ o l d Char ac teri s t ics Whit e B lack H i s p anic S ingl e Household size I adults I old children I young child r e n tl vehi c les No veh i c l e Income ca t ego ry l ocation Char acteris tics Cent r a 1 c ity Urbanized-area size Population density tlorth Eas t tlorth Centra I Sou th T r i p Characte r istics Dark Peak. hour s Carpool D i stance Wint e r Speed Work-related Shopping Other famil y/personal Medic al V i siting friends/relat ives Oth er soc i al/recreat i onal Vehicle C h aracteri s tics Vehi cl e age Import status Definition 1 for p ersons age 65 yea rs o r older: 0 o therwise 1 fo r p e r sons age 24 years or younger: 0 o therwise 1 for mal es: 0 otherwi se 1 for p erso n s w i th above hig h s c h oo l educat1on: 0 othenw1se 1 f or person s i n t h e l abor force : o otherwise 1 for W h i t e households: 0 otherwise I f or hOuseholds: 0 ot herwise I for Hispani c 0 ot herwis e I for person s f rom single-person household s : o ot herwi se 0\lmber or hou seho l d r es i de nts number of adult house h old reside nt s number of househol d r es i dents age 5 t o 22 years number of househol d res ident s age 5 year s o r younger number o f hous ehold automobi les a n d 1 f o r househo lds with no automobiles o r t r u cks: 0 otherwise l evel of household tncorne on a sca l e from 1 to 1 7 1 for households in cent r a l cities : 0 otherwise s 1ze of an urbanized area on a s cale from 1 t o 5 persons per 1 0 0 0 square miles for household zipcode area 1 fo r households i n the Nort h East re g ion: 0 otherwise 1 for households in the North Central region: 0 otherwi s e 1 fer households in the Sovth regi on: 0 otherwise I if s t a r te d aft e r s unset and befor e sunr i se: 0 otherwis e I i f s t ar te d from 6 :30-9:00 a.m. o r 3:3 0-6:00 p .m.: 0 otherwise I if made frcxn 4:00 />1!1 Saturday-3:59 N1 Monday: 0 o therwise I if ther e are more t han o n e occupant: 0 otherwis e repo r te d d i s tance in miles 1 1 f i n December. January. or Febr u ary: 0 otherwis e r ot i o o f reported di stance and duratio n in m i l e s p e r hour (mph) 1 fo r commut1ng and othe r work -re late d purposes: 0 otherwise I for shopping purpose: 0 otherwise 1 fo r o the r fam1ly o r persona l busi ness: 0 o therwise 1 for med1cal purpose: 0 oth erwise 1 for purpose s of visiting f r iends or re l atives: 0 otherwise 1 for othe r soc ia l or recrea t ional purposes: 0 o t h erwise the d i f f e rence between 1990 and vehi c l e model y ear 1 tor veh icles wit h foreiqn brand names: 0 o therw\Se 7

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Chapter 3 THE EFFECTS OF AGE ON HOW MUCH THE ELDERLY DRIVE This chapter examines the effects of age on the amount of driving exposure by the elderly. Three measures of driving exposure are considered These measures are the number of vehicle miles driven by individual drivers on the travel day, the number of vehicle trips taken by individual drivers on the travel day, and the distance of individual vehicle trips on the travel day Each of these measures is first tabulated by driver age group and labor force participation. Regression analysis is then used to isolate the effects of age on each of these measures. NUMBER OF DAILY VEHICLE MILES DRIVEN TABULATION Table 3 1 tabulates the average number of vehicle miles driven on the travel day by driver age group and labor force participation. On average, elderly persons in the labor force drive about 19 miles a day and those not in the labor force drive about 10 miles a day. In comparison, mid-aged persons in the labor force drive about 29 miles a day, and those not in the labor force drive about 16 miles a day; and young persons in the labor force drive about 27 miles a day and those not in the labor force drive about 3 miles a day. Table 3.1 Average number of dail y vehicle mile s driven b y dr iver age group Drive r Age Group All Young (Age < 24l Mid-Aged (25l All O rivers 19.23 9.45 25.87 11. 4 4 I n Labor Force 28.02 26.70 28.63 19.27 Not i n Labor Force 8.07 2.73 15.82 1 0 .29 Source : Computed from the Person File a s the wei ghted average of total vehicle mil es driven by each responding driver on the travel day. REGRESSION Regression analysis is used to isolate the effec ts of age on the number of vehicle miles driven by individual elderly drivers on the travel day. Regression analysis i solates these effects by inclu ding age an d other personal, household, and location characteristics of the elderly drivers as control variables. The number of vehicle miles driven by indivi dual drivers i s the dependent va riable The age and other characteristics of individual drivers are the explanatory variables. 8

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Model The first candidate model for this regreS$ion analys i s wou ld be the s tand ard linear regression model. This model can be defined as f ollows: y1 = {J1x1 + u (1) where y1 is the dependent variable ; i indicates an observation in the data; p is a column vector of unknown parameters; x, is a co lu mn vector of known values of the explanatory variables for observation i; and u, is a disturbance term for observation i that is independently and normally distributed across observations with a zero mean and common variance If the assumpt i ons of this model are not met, parameters estimated from the ordinary least squares method may not have properties such as consistency or efficiency. The current problem violates the assumpt i on that the disturbance term has a zero mean. About 40 percent of the responding drivers reported no veh i cle mile s driven on the travel day. This situation fits the Tobit model which originally was formulated to analyze survey data of consumer expenditures on durable goods. Most households report zero expenditures on major durable goods during any year Among those households that report any such expenditures, however the amounts vary wide l y The Tobit model can be defined as follows: y, = P'x, + u, if RHS > 0 y1 = 0 otherwise (2) where RHS refers to the right hand side and the other sym bols are as defined in the standard linear regression model in equation (1 ). The ordinary least squares method in this situation leads to inconsistent estimates of the unknown parameters. Consistent estimates i n the T obit model can be obtained with the maximum likelihood or Heckman two-stage method. The Heckman method is easier to compute, but less efficient. Therefore the maximum likelihood method is used for this analysis.' Results Many factors could affect the number of vehicle miles driven on a given day by individual drivers. These factors in clude the characte r istics associated wit h the drivers as well as the cost of driving. While the 1990 NPTS contains a set of personal, household, and location characterist ics of the drivers, i t does not, however, include information on the cost of driving. As a result, the cost of driving is approximated by the statewide average r efiner/reselle r sales price of motor gasoline plus state gasoline tax i n 1990." Th is cost of driving ignores any variation i n the refiner/reseller sales price of motor gasoline within a state and in non-state loca l gasoline taxes. This cost of driving also ignores other componen t s of driving costs. This cost of driving, in cents per gallon, will be referred to as gasoline price. The results are shown in Table 3.2 The fi r st co lum n li sts the explanatory variables by category The second column l ists the est imated coefficients measuring the marginal effects 9

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T ab l e 3 2 T obi t analysis of daily vehi c l e m i les d r iven Exp lana t ory Variables Coeff i c ients x'-Stati stics Personal Character istics Age> .8392 5.74 Age< 2 4 -18 .7563 315.00 Male 8.04 05 104.79 E d ucation> h i gh schoo l 11.8649 193.71 Wor ker 33.1366 1080. 7 4 H ousehold Charac t e r i s tics White 5 .6748 9.19 Black -0 .0913 o ooH i span i c 0 .2098 0 01" Income category 0 2 1 94 5 02" S i ngle 4.4484 8 .03 # adults -0 .6361 1.24" # old children 3.6891 87.12 #youn g c h i ldren 3 9341 21.60 No vehicle 57.3373 1 84 .10 L ocation Char ac t e r>stics Cent ra 1 c ity .7091 9.93 Urbani zed-a r ea size .2500 0 .66" Population density -0 .5677 104.20 North East .3290 1.00" North C entra 1 1.2319 1.07" S o uth 1.2448 1.07" W inter -2.4424 7 .44 Weekend 6.7313 6138 Gasol i ne Price 0 .1587 3.83 -3. 1058 0 .15" Log Likel ihood a t convergence -64021 Number o f observat i ons 19.024 Proport i o n of observa t ions with zero v ehicl e m i l e s 40% Source : Est imat e d from th e Perso n Fil e us1ng the max1mum l i kelihood methOd with the SAS LIFEREG p r o c ed ure. T h e d ep enden t variabl e is total number of vehic l e miles driven on the t rave l day by eac h r espo n d ing dri ver Whet her a coefficient differs fro m ze r o is l abe l e d as follows: s i gni f i can t at the 5 p e rcen t l evel: Ins ignificant at the 1 0 percent l eve l : others s i gnifican t at the 1 perce n t l e vel 10

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of an explanatory variable on the dependent variable while holding constant other explanatory variables The last column lists the corresponding chi-square (x2 ) statistics, indicating the statistical significance of the explanatory variables. At the bottom are the log likelihood at convergence, the number of observations used in the estimation, and the proportion of observations wijh zero miles.' Two issues are involved in the interpretation of the results. First, the sign of a coefficient in a Tobit model measures t he direction of changes in the dependent variable from a change in the corresponding explanatory variab le. But to compute the magnitude of these changes in the dependent variable is not straightforward. The interpretation here focuses on the signs. The second issue involved in the interpretation of the results concerns dummy variables. Since the model includes a constant term, the dummy variable coefficients are interpreted relative to the omitted category For example, the dummy variable for male drivers is included, but the dummy variable for female drivers is omitted. The omitted category becomes a benchmark. The dummy variable coefficients for the remaining categories tell whether or not each of the remaining categories differ from this benchmark and, if so, by how much. There are two types of dummy variables : those involv ing two categories and those involving more than two categories. The two-category dummy variables include gender, educational attainment, labor force participation, Hispanic status, single status, location in an urbanized area, month-of-year, and day-of-week. The multi-category dummy variables include age, race, and census region. The omitted category for age i ncludes those persons between the ages of 25 and 64 years; the remaining categories include those persons age 24 years or younger and those persons age 65 years o r older. The omitted category for race Includes those persons who are neither INMe nor Black; the remaining categories include White persons and Black persons. The omitted category for census region is the West; the remaining categories include the North East, North Central, and South regions. The results indicate that the coefficient of the elderly dummy variable is -3.8392 and differs from zero at the 5 percent level. Thus, other things being equa l the elderly drive fewe r miles than th e mid-aged. The other variables are organized into two groups for interpretation. The first group includes those variables whose coefficients differ from zero at up to the 10 percent level. The results indicate that other things being equal, persons in the labor force drive more miles than those not in the labor force; males drive more mites than females; Whites drive more miles than drivers who are neither White nor Black; Blacks drive fewer miles than Whites: persons with higher household incomes drive more miles; and persons from households with more children under fiVe years old drive more miles. In addition, the young drive fewer mites t han the mid aged ; persons from households without vehicles drive fewer miles than those with vehicles; persons livin g in areas wijh higher !)O;:JUI:Ji ion densities drive fewer miles; persons living in central c"ies drive fewer miles than those living outside central cijies; and the number of daily vehicle miles driven by individual persons decreases with an increase in gasoline price. 11

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The sec ond group include s those variables whose coefficients do not differ from zero at the 10 percent level. The resu lts ind icate that, other things being equal Blacks drive the same number of daily vehicle miles as those who are neither White nor Black; Hispanics drive the same number of daily vehicle miles as non-Hispanics; the s ize of an urbanized area does not affect the number of daily vehicle miles driven by in dividua l persons; and census region does not make a difference in the number of dai ly vehicle miles driven by individual persons. NUMBER OF DAILY VEHICLE TRIPS The number of vehicle miles driven combines the number and distance of vehicle trips. The previous section has shown that the elderly drive fewer miles than the mid-aged. Does this result imply that the elderly take shorter trips as well as make fewer vehicle trips than the mid aged? The prov i des mixed evidence . The number of vehicle trips taken on the t ravel day by individual drivers and the distance of individual vehicle trips are examined separately using both tabulation and regress ion ana lys is. TABULATION Table 3.3 tabu lates the average number of vehicle trips taken on the travel day by driver age group and labor force participation. On average, elderly persons in the labor force drive 2.56 vehicle trips per day and those not in the la bor force drive 1.64 vehicle trips per day. M id aged persons in the la bor force drive 2.99 vehicle trips per day and those not in the labor force drive 2.22 vehicle trips per day. Young persons in the la bor force drive 2.92 vehicle trips per day and those not in the labor force drive 0 .35 vehicle trips per day. Table 3.3 Average number of dail y vehi c l e t r ips by driver a g e group Driver Age Group All Drivers In Labor Force Not i n Labor Force All 2.17 2 97 115 Young -651 1.76 2.59 1.64 Sou rce: Calculated from the Person F ile as the weighted average of the number of vehicle trips driven by each responding d river on the travel day. REGRESSION This regressi on analysis is similar to that for the number of vehicle m iles driven by Individual persons in the previous section. The unit of observation is individual drivers. The 12

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same set of explanatory variables are used. As mentioned in the previous section, about 40 percent of the responding drivers reported no vehicle miles on the travel day. Thus the T obit model in equation (2) is used along with the maximum likel i hood method for estimation The results are shown i n Table 3 4 The results indicate that the coefficient of the elderly dummy variable does not d i ffer from zero at the 10 percent leve l Thus other things being equa l the elderly drive just the same number of vehicle trips per day as the mid-aged. The other explanatory variables are organized into three groups for interpretation. The first group i ncludes those variables whose coefficients differ from zero at up to the 10 percent level. The results indicate that other thi ngs being equal, persons in the labor force drive more vehicle trips than those not in the l abor force; persons with more than a high school education drive more vehicle trips than those wi t h less education; Whi t es drive more vehicle trips than those who are White not Black ; Blacks drive fewer vehicle trips than Whites; persons living with ch i ldren under five years old drive more vehicle trips than those not living with children under five years old; and persons from single-resident households drive more vehicle trips than those from mul ti-resident households. I n addition the young drive fewer vehicle trips than the mid-aged; persons from households without vehicles drive fewer vehicle trips than those from households with vehicles ; people drive fewer vehicle trips on weekend days than on weekdays; the number of daily vehicle trips taken by i ndividual drivers decreases wi t h an increase in the number of adults in a household ; the number of da i ly vehicle trips taken by individual drivers decreases with an Increase In the population density of a zip-code area; and the number of daily vehicle trips taken by i ndividual drivers decreases wilh an increase in the size of an urbanized area The second group includes those variables whose statist i cal significance changes in explain i ng the number of vehicle m i les driven and vehicle trips taken by individual drivers on the trave l day. The results in Tables 3.2 and 3.4 indicate that other th i ngs being equal males drive more miles than females, but not more vehicle trips ; household income affects the number of miles driven, but not the n umber of vehicle trips; gasoline price affects the number of m i les driven, but not the number of vehicle trips ; and l iving in central cities affects the number of miles driven but not the number of vehicle trips taken In addition. the size of an urbanized area has no effect on the numbe r of miles driven. but affects the number of vehicle trips taken by i ndividual drivers The third group inc l udes those variables whose coefficients that do not differ from zero at the 10 percent level in explaining both the number of vehicle miles driven and the number of vehicle trips taken by individual drivers on the travel day. The results in Tables 3 2 and 3.4 indicate that. other things being equal Blacks drive the same number of miles and take the same number of vehicle trips as those who are neither While no r Black; Hispanics drive the same number of miles and take the same number of vehicle trips as non Hispanics; and census region does not make a difference in exp l aining the number of miles driven or the number of vehicle trips taken by individual drivers. 13

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Table 3.4 T obit analysi s of number of daily vehicle trips Explanat o r y Variables Coeffici ents x'-Sta tistic s Personal Character isti cs Age>-65 -0.0737 0. 40" Age< 18436 572.58 Mal e -0.0237 0.17" Education>high school 1. 0692 292.43 Worker 2 .6303 1284.1 5 Househol d Char acterist i cs White 0 .284 9 4.36' 8lad 0.0225 0.02' H i spanic -0.1387 0 .91' Income category -0.0062 0 76' Sing le 0.401 9 12.21 II adults 0 .0760 3.33' II old chi ldren -0.1542 28.91 #young children 0 .3227 1 8 .50 No vehicl e -5.2585 295.10 Location Characterist ics Central c ity -0.0535 0 73' U rbanized-area size -0.0835 13.88 Populatio n densi ty .0495 148.53 North East -0.0026 o .oo North Centra 1 0.2678 9.45 South 0 1948 4.89' W inter -0.1429 4. 78" Weekend 9011 205.86 Gasol ine Price 0 .0031 0 .27' 0 .7806 1.74 Log L i k elihood at convergence -35432 Number of observations 1 9 .024 Proportion o f observat ions w i th zero veh icle m i l es 40% Source: Estimated from th e Person F il e using the maximum method with the SAS L I FERE G p rocedure Whether a coeff i cie n t differs from zero is marked as fol lows: signif ic ant at the 5 percent level: i n sign i ficant a t the 10 percent level : o thers signi f i cant at t h e 1 p ercent level. 1 4

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DISTANCE OF DAILY VEHICLE TRIPS TABULATION Table 3.5 tabulates the average distance of vehicle trips taken on the travel day by driver age group and trip purpose. For elderly drivers, the average distances are 6.55 miles for all trips, 8.30 miles for work trips, and 6.43 miles for non-work trips. For mid-aged drivers, the average distances are 9.25 miles for all trips 11. 54 miles for work trips and 8.22 miles for non work trips. For young drivers the average distances are 8 .91 miles for all trips, 9.98 miles fo r work trips and 8.54 miles for non-work trips For all drivers, the average distances are 8.98 miles for all trips, 11.23 miles for work trips and 8.10 miles for non-work trips. Tabl e 3 .5 Average distance of dail y vehicle t rips by driver age group Dri ver Age Group All Trips Work T rip s Non-Work Trips All 8.98 11.23 8. 1 0 Young (Age<> 8.91 9.98 8.54 Mid-Age d <2565> 6 .55 8.30 6.43 Source: Calculated from the Travel Day Fil e as the wei g hted average o f d i s tances of individual vehicl e trips on the t ra v e l day in miles. REGRESSION As with the models developed for the number of vehicle miles driven and the number of vehicle trips taken by individua l drivers on the travel day, the purpose of this regression analysis is to isolate the effects of age on the distance of individual vehicle trips taken by e l derly drivers on the travel day Model The regression analysis in this section differs from those in the previous sect i ons in two important aspects. First, while a large proportion of respon ding drivers reported no vehicle trips on th e travel day, the variable measuring the distance of vehicle trips does not have this problem Instead of the Tobi t model in (2), the standard linea r regression model in (1) is used along with the weighted least squares method for estimation Second, while the unit of observation in the previous sections is indiv idual drivers, the unit of observation in this section is individual vehicle trips As a result an additional set of explanatory variables measuring trip characteristics is also included These additional variables incl u de time-of-day, whether the driver carried any passengers day-of-week month-of-year and the purpose of a vehicle trip 15

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Results The results are shown in Table 3.6. The interpretation of the standard linear model is straightforwa r d The coefficient of an explanatory variable measures the expected change i n the va l ue of the dependent variable from one unit change in the exp l anatory variable, while holding other explanatory variables constant. Another issue of interp r etation is the set of dummy variables that measures trip purposes The 1990 NPTS classifies trip purposes into ten categories. Four of these categories are omitted from the model: trips for school o r church trips fo r vacation, trips for pleasure driving and trips for other purposes. The r emaining six categories are included i n the model. As a resuH, the coeffic i ents of the dummy variables for these remaining categories are interpreted re l ative to the omitted categories. The resuHs indicate that the coefficient of the elderly dummy variab l e i s -1.0471 and differs from zero at the 0.01 percent level. Thus other things being equal the elderly drive about one mile shorter per trip than the mid-aged. The othe r variables are organized into two groups for i nterpretation. The first group includes those variables whose coefficients differ from zero at up to the 10 percent l evel. The results indicate that, other things being equal male drivers take longer trips than female drivers: drivers in the labor force take longer trips than those not in the labor force; WMe drivers take longer trips than those who are neither White nor Black ; Blacks take trips of shorter distances than those taken by Whites; drivers with highe r household incomes take longer trips ; and drivers liv i ng in larger urbanized areas take longer trips In add i t i on drivers l iving in central c i ties take shorter trips than those living ou t side central cities; the distance of vehicle trips decreases w ith an increase in gasoline price: drivers living i n areas with h i gher population densities take shorter trips; trips for worl<-related purposes and for visHing friends or relatives are longer than trips for those purposes that are omitted from the model; and trips for other remain i ng purposes are shorter than trips for those purposes that are omitted from the model. The second group includes those variab l es whose coefficients do not differ from zero at the 10 percent l evel. The results indicate that other t h ings being equal young drivers take trips that are just as long as those taken by mid-aged drive r s: wi nter trips are just as long as non winter trips ; night trips a r e just as l ong as day trips ; peak trips are just as long as off-peak trips: Black drivers take t rips that are just as long as those taken by drivers who are neither White nor Black; Hispanic drivers take t rips that are jus t as long as those taken by non-Hispanic drivers; and drivers in the North East or South reg i ons take trips that are just as long as trips taken by those in the West. 16

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Tabl e 3.6 Weighted regression of d ista n ce of daily vehicle trip s Explanatory Variabl es Coefficien t s t-Statistics Personal Characteristics Age>-65 -1.0471 -2.76 Age<-24 -0.2702 -1. 18' Hal e 2.2187 13.01 Education>high school 0 6365 3 .58 Worker 0.5928 2.46" Household Characteri stics White 12739 3. 1 9 Black 0.6 1 9 1 1.29' Hispanic 0.0445 -0.11' Income category 0 .1030 5.09 L ocation Characterist ic s Centra 1 ci t y -0.3041 -1.63' Urbanized-a rea s ize 0 .3978 5.92 Population densit y -0.0406 -2.39' North East -0.0529 0 .19' North Central -0.6060 -2.49' South 0 .1080 -0 .47' Gaso 1 i ne Pri ce 0.0 400 -2.19 Trio CharacteriS t ics Oark 0.2013 0 .95' Peak hours 0.2131 1.17' Weekend 1.4526 7.20 Winter -0.2125 -1.08' Carpool 2.1489 11.38 work-re l ated 0 .7136 1.84' Shopping -5.6096 -14.52 Other family/personal -3.4523 -9.11 M edica l -1.5511 -!. 74' V isiting frien ds/ relatives 1.2935 2.98 Other social/recreational -1.4867 3.56 !;.Qnmnt 7.8105 54.34 F Stat isti c 56.61 Mean of dependent vari able 8.30 Number of observations 43.936 Source: E stimated b y Author from the Travel Day File using th e weighted least squares method. Whether a coefficient d i ffers f rom zero i s labeled as f o l las : sign i ficant at the 5 percent level: sign i ficant at the 1 0 percent level : ins ig nif ic ant at the 10 percent level : others s ig nificant at the 0 .01 percent level. 17

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Chapter 4 THE EFFECTS OF AGE ON WHEN THE ELDERLY DRIVE This chapter examines the effects of age on driving at night or during peak hours by the elderly. Night includes the hours after sunset and before sunrise. Peak hours include 6:30-9:00 a.m. and 3:30-6:00 p.m Whether a vehicle trip was taken at night or during peak hours is detennined by its start time. Driving at night is examined first, followed by an examination of driving during peak hours. For each analysis, the percent of vehicle miles driven by time of day is first tabulated by driver age group and trip purpose. Logit analysis is then used to isolate the effects of age on the elderly s p roba bility of driving at night or during peak hours. DRIVING AT NIGHT TABULATION Table 4.1 tabulates the percent of vehicle miles drive n at night by driver age group and trip purpose The elderly drive about 18 percent of their miles at night for both work and non work trips, while the mid-aged drive about 29 percent of their miles at night for work trips and 23 percent for non-work trips. The young drive about 29 percent of their miles at night for work trips and 25 percent for non-work trips. T ab l e 4 1 Percent o f m i l es driven at night by driver age group Drive r Age Group A l l T r ips lolork Tr ips Non-work Trips All 28.66% 23.51% Young (Age<4) 26.12% 29.03% 25.74% Mid-Aged l25 24.62% 28.84% 22.94% Elderly (Age>-65> 18.34% 18.43% 18.31% Soorce: Calculated from t h e Trave l Day File. Each nuni>er represents total m iles dr iven by drivers of a g iven group at night as a percentage o f total m i les driven by these dri ve r s all day. REGRESSION The purpose of this regress ion analysis is to isolate the effects of age on driving at night by the elderly, while holding constant a set of the elderly's personal, household, and location characteristics. 18

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Model Similar to the regression analysis of the distance of vehicle trips in the previous section, the unit of observation is individual vehicle trips This regression analysis, howeve r differs from that for the distance of vehicle trips in four aspects. First. the dependent variable here i s binary, indicating whether a vehicle trip on the travel day started at night. One commonly used regression model for a binary choice problem is the legit model, in which the probability of choosing to drive at night has the form. If P is the probability of driving at night x is a column vector of the values of explanatory variables, and p is a column vector of parameters, then: e"'' 1 -+ (3) Second, speed may differ systematically by time of day. In addition to a sim ilar set of explanatory variables used in the model for the distance of vehicle trips speed is also included In this analysis Third, the ordinary least squares method does not apply here. Ins tead, the maximum likelih ood method is used for estimation Fourth, several variables are excluded because convergence could not be reached when these variables are included. These excluded variables are Black, Hispanic, and the census regions. The reason that these particular variables are chosen to be excluded is that they are thought to be less important than others in the decision of driving by time of day. Results The results are shown in Table 4.2. The coefficients in this model are interpreted differently from those in a standard linear or Tobit model. First, an inc rease in a variable wilh a negative coefficient decreases the odds ratio of driving at night. The odds rat i o of driving at night is P/(1-P'). where P is the probab ilily of driving at night. Second, the exponential value of the coefficient of an explanatory variable determines the percent change in the odds r atio of driving at night from one unit change in that explanatory variable. For example, the dummy variable for male drivers has a coefficient of 0.3070. Its effect on the odds ratio of driving at night is 1 OO'(e0 30" -1) = 36 percent That is, males' odds ratio of driving at night is 36 percent higher than females' odds ratio of driving at night. The resuHs indicate that the coefficient of the elderly dummy variable is -0.2183 and differs from zero at the 0.01 percent level. Thus, other things being equal, the elderly are less likely to drive at night than the mid-aged. In fact. the elderly's odds ratio of driving at night is 20 percent lower than the mid-aged's odds ratio of driving at night. The other variables are organized into two groups for interpretation The first group has posi t ive coefficients. The results indi cate that, other things being equal, the young are more likely to drive at night than the mid-aged; males are more likely to drive at night than females; persons in the labor force are more likely to drive at night than those not in the labor force; 19

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Table 4.2 Logi t anal ysis o f dr iving a t night Explanatory Variabl e s Coeffi cient s :t'Statistlc s Persona 1 Charac:teri sti cs Age>0 .2183 22.02 Age< 0.3 124 134.87 Hale 0.3070 223.85 E ducat ion>high schoo l -0.1193 33.03 Worker 0.3630 149.07 Household Character istics Whit e -0.1122 14.90 Income category 0 .0013 23.57 Locatjon C haracterist ics Central city 0.0851 15.56 Urbani zedare a s ize 0.0281 13.09 Popu lation densi ty 0 .0046 6 .6 6 Tri p Character istics Weeken d 0 .2940 140.46 W i nt e r 0.8139 1381.84 Work-related 0.194 6 17.14 S h o p p ing -0.1376 7.97" O t her fami ly /personal -0.16 1 5 11.46 Medical -1.0784 51.13 Visiting friends/ r e l atives 0 .6065 137.20 Other socia l /recreational 0 6568 174.73 Speed 0 0051 63.91 Con stant 2 .6550 1086.12 x'-Stat istics 3499 N umbe r of obser vat ions 57.31 2 Number of o b servations driving at night 14.135 Source: Estimated from t h e Travel Day File using the maximum lik e l ihood method wit h the SAS LOGISTIC procedure. Whet her a coefficie n t d iffers from zero is marked as follows: sig n i fican t at the 1 p ercent leve l : others sig n i f icant at the 0 .01 percent level. 20

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pers o n s living i n central cities are more likely to drive at night than those l iving outside central cities; the probability of driving at night increases w ith an i ncrease in household income, the size of an urbanized area and the popu l ation density of a z i p-code area; and trips for wori<-related purposes. visiting friends or relatives and other social or recreational purposes are more likely to be taken at night than trips for those purp o ses that are omitted from the model. The second group has negative coefficients The results indicate that other things being equal, persons with more than a high scho o l education are less likely to drive at night than those w ith less educat i on; Whites are less likely to drive at night than non-Whites; and trips for shopping, othe r family or personal business. and medical purposes are less likely to be taken at night than trips for those purposes that are omitted from the model. Note that the omitted category for race in th i s ana l ysis is non-Wh i tes. DRIVING DURING PEAK HOURS TABULATION Table 4.3 tabulates the percent of vehicle miles driven during peak hours by driver age group and trip purpose The drive about 28 percent of their miles during peak hours for non-wori< trips 57 percent fo r wori< trips, and 3 0 percent for all trips The mid-aged drive about 31 percent of their miles during peak hours for non-wori< trips 59 percent for wori< trips and 39 percent for all trips The young drive about 38 percent of their miles during peak hours for non wori< trips, 50 percent for w ori< trips. and 40 percent for all trips. Table 4.3 Percen t of m i 1 es driven d uring pe ak ho u r s b y driver age g ro u p Age Group All Tr i p s Work Trips Non-Work Tr i p s All 38.7U 57.39% 33.5U Young (Age<24 J 39.67% 49.84% 38.36% Hid-Aged (25< Age<4) 39.26% 58.84% 31.36% Elderly > 30.02% 56.69% 28. 16% Source: Calc ulated f rom the T r avel Day Fil e Each nwnber represents total miles d r iven b y drivers of a g iven group dur ing peak hour s as a perce n tage of total miles driven b y these drivers all day REGRESSION The regression analysis of driving during peak hours is similar to that for driv ing at night. Again the dependent variable is binary indicat ing whethe r a vehicle trip on the travel day started during peak hours. The same set of exp l anatory variables are included as i n the regression 21

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analysis for driving at night. The model is used along with the maximum likelihood method for estimation The resuhs are shown in Tab le 4.4 The results indicate that the coefficient of the elderly dummy variable is -0 .1251 and differs from zero at the 1 percent level. Thus, other things being equal, the elderly are less likely to drive during peak hours than the mid-aged. In fact t he elderly's odds ratio of driving during peak hours is about 12 percent lower than the odds ratio of driving during peak hours by the mid-aged. This difference in the odds ratio of driving during peak hours between the elderly and mid-aged is smaller than that for the odds ratio of driving at night. This change in the difference is consistent that the elderly find driving at n ight more problematic than driving during peak hours. The other variables are organized into three groups for interpretation. The first group includes those variables whose coefficients are positive and differ from zero at the 10 percent level. The results indicate that, other things being equal, persons in the labor force are more likely to drive during peak hours than those not in the labor force; persons with more than a high school education are more like ly to drive during peak hours than those with tess education; weekend trips are more like ly to be taken during peak hours than weekday trips; and work trips are more likely to be taken during peak hours than trips for those purposes that are omitted from the model. The second group includes those variables whose coefficients are negative and differ from zero at the 10 percent le ve l The resuhs indicate that, other things being equal, the young are less likely to drive during peak hours than the mid-aged ; mates are less likely to drive during peak hours than females; trips for shopping, other family or personal business medical, visijing friends or relatives and other social or recreational purposes are less likely to be taken during peak hours than trips for those purposes that are omitted from the model. The last group includes those variables whose coefficients do not differ from zero at the 10 percent level. The results indicate that, other things being equal, Whites are just as likely as non-Whites to drive during peak hours; househo ld income or the size of an urbanized area does not affect the probab ility of driving during peak hours ; persons living in central cities are just as likely as those living outside central cities to drive during peak hours; and winter trips are jus t as likely as non-winter trips to be taken during peak hours. 22

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Table 4.4 Logi t analysis of dr iving during hours Expl anatory Vari ables Coefficients Personal Qlaracter i sti cs Age>-65 0.1257 1 0.15 Age< .1678 41.81 Hale 0. 0803 1 8.73 Education>high school 0.044 8 5 .66' 0.1332 26.49 House hold Characteri stics White 0 0 153 0 32' I ncome category .00 01 0.30" Location Characterist i c s Cen t ral city 0.0082 0.18" U rbanized-area si z e 0.0110 2 .51" Popul a t ion density .0031 3.13' T r i p Character istics W e e kend 0.2967 172.88 W i nter .00 2 2 0. 01" Work-rel ated 0.7208 320.42 Shopping .4303 108.54 Other family/persona l 0 .2020 25.45 Medical 0 .3750 16.08 V i siting f r iends/rela t iv e s 4746 98.67 Other social/recreational .4631 106.06 Speed 0033 31.60 Constant .8658 151.75 x'Statistics 41270 Number of o b se r vations 55.610 Number of observations drivin g at night 21.604 Sour ce: Estimated by f rom the Travel Qay Fil e using the maximum li kelihood method w i th the SAS LOGISTIC procedure. W h ether a coeffic ient differs from zer o is l abeled a s foii
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Chapter 5 THE EFFECTS OF AGE ON HOW THE ELDERLY DRIVE Chapters 3 and 4 have shown that age affects how much, as well as when the elderty drive. This chapter examines t he effects of age on how the elderly drive. Four aspects are considered. These include driving speed, driving on highways, vehicle size, and the number of passengers carried. SPEED This section examines the effects of age on the driving speeds of the elderly Do the elderly drive at l ower speeds lhan others? If they do, do they drive on roads with lower speed limns? Or do they drive slower than others on roads with the same speed lim its? The 1990 NPTS can be used to shed light on whether the elderly drive slower than others on limited access highways. The 1990 NPTS does not, however, include the informat ion necessary to test whether the elderly drive on roads with lower speed limits than others. In the fo llowing analysis, speed is first tabulated by driver age group and trip purpose. Regression is then used to Isolate the effects of age on the driving speeds of the elderly. This analysis is done separately for all roadways combined and for limited-access h igh ways TABULATION Table 5.1 tabu lates the average speed f o r vehicle trips using all roads by driver age group and trip purpose. The elderly drive at an average speed of 22 mph for all trips, 24 mph for work trips, and 22 mph for non-work trips. The mid-aged drive at an average speed of 29 mph for all trips, 31 mph for work trips, and 28 mph for non-work trips The young drive at an average speed of 32 mph for all trips, 34 mph for work trips, and 31 mph for non-work trips. Table 5.1 Average speed on all roads by driver age group Oriver Age Group All T r ip s Work Trips Non-Work Trips All 28.69 31.58 27.55 Young (Age<) 31.83 34.39 30.93 Mid-Aged (25) 22.05 24.35 21.89 __.. __ __;:..::..;.:..::..;. Source: calculated from the Trave l Oay File as the weighted average of the speeds of individual vehi c le tr1ps. The speed of a trip I s measured as the ratio of its reported d i stance and d uratio n in miles per hour (mph). 24

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Table 5.2 tabulates t he average speed for vehicle trips using limited-access highways by driver age group and trip purpose. As expected the average speeds for trips using limited access highways are higher than those for trips using all roadways On average, the elderly drive at about 34 mph for all purposes, 36 mph for work trips and 33 mph for non-work trips The mid-aged drive at about 3g mph for wor\<.trips, non-wor\<. trips, and all purposes. The young drive at about 44 mph for all trips 44 mph for work trips, and 42 mph for non-work trips. All persons as a group drive at about 39 mph for both work and non -work trips. Tabl e 5.2 Average speed on li m i ted -access highways by driver age group Oriver Age Group All Trips Trips Non-Work Trips All 3 9.22 39.16 39.31 Young (Age<4) 43.96 44.47 4 2.31 H i d-Aged <25) 33.77 36.45 33.45 Source: Calculated from the sawple of private-vehic l e trips '" the Travel Oay F ile as the weig hted a v era g e of t h e speeds f o r indivldual t r1ps thl s sample The d'stance of each tr'P i n this sample 1 s broken down b y roadway c lassifi cation. REGRESSION This regression analysis is s imilar to that for the distance of vehic l e trips in Chapter 3 The unit of observation is individual vehicle trips. The dependent variable is the speed of in dividua l vehicle trips measured as the rat io of reported distance and duration in miles per hour. The same set of explanatory variables are included as in the analysis of the distance of vehicle trips except gasoline price. The standand linear regression model in equation (1) is used along the ondinary least squares method for estimation. The results are p resen te d in Table 5.3 The model for trips using limited-access highways is shown in the second and third columns. The model for trips using all roadways is shown in the l ast two columns. The results indicate that the elderly drive at lower speeds than the mid-aged for trips using all roads as well as for trips using limited-access h i ghways. The model for all roadways indicates that, other things being equal the elderly drive 3. 9 mph slower than the mid-aged for trips using all roadways. The model for limi ted-access highways indicates tha t, other things being equal, the elderly drive 3. 7 mph slower tha n the mid-aged for trips using highways Thill (Jther variables are organized into fou r groups for interpretation Those in the first group have a positive effect in both models. The results indicate that, other things being equal the young drive at higher speeds tha n the mid-aged for bOth trips using all roa dways and trips 25

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Table 5.3 Weighted regress ion of speed of vehi c le trips Lim i ted-Access Highways All Roads Explanatory Variables Coefficients t-Statistics Coefficients t Stat lst1CS Personal Age>-65 -3.7258 .24 3.8598 .30 Age< 5. 1825 6 .46 2.9221 14.13 Hale 2.6885 4.60 1. 3154 8 .55 Education>high school 0 .9070 1.47' 1. 0727 6.69 Worker 3.6277 384 1.7807 8.19 Househol d Characteristics White 3.3632 2 .62 1.7256 4.81 Black 5.0459 3 .35 0.6354 1.47" H i spanic 3.1640 2.25' -0.3465 -0. 94" Income category 0.1313 1.90' 0.2054 11.25 Location Characterjstjcs Central city 1.0178 1.64' 0.8086 4 .80 Urbanized-area size 0 .0541 0 .21" 0.1829 3.03 Popul ation den s i ty 0.3490 76 0.2660 -17.14 North East 0 .1813 0 .21" 1.2205 5.24 North Centra 1 0.4542 -0.54" .4757 .17" South .6494 -2.28" 0.3476 1.70' Tri p Character i stics Dark 0 .3544 0 .49' 1.3667 7.17 Peak hours 2.3635 3 .76 0 .5709 -3.47 Joleekend 2.5473 3.59 1.7567 9.62 Carpool 0 .5986 -0 8i" 1. 2350 7 2 4 Joli nter -0.4823 0. 73' -0.4734 .68 Jolork re 1 a ted 1.8450 1.44" 2.6 185 7 .48 Shopping 0.7108 0.49" -3.0883 .84' Other fami ly/pe rsonal 1. 7029 1.26 -1.1646 -3.40 Medical 8.401 2 2.78 2.1862 2.72 V isiting frien ds/relati v e s 6 7746 4 66 2.4156 6.1 4 Other social/recreational 2.3077 1.56" 0 .0315 o .o8" 27.2783 11.82 21.3926 35.67 FStati stlc 9 110 Mean of dependent variab l e 38.36 27.64 Number of observations 2 .431 43.431 Source: Estimated from the Trave l Day File using the weighted l east squares method. llhr: tf.er coefficie n t differs from zero i s l abeled as follows: signif icant at the 5 percent level: signi f icant at the 10 per c ent leve l ; ins i gnificant at the 10 percent level: ot h ers signif ic ant a t the 1 percent l e vel. 26

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using limited-access highways Similarly, males drive at higher speeds than females; persons with higher household incomes drive at higher speeds; weekend trips have higher speeds than weekday trips; and trips for medical and visiting friends or rela tives have higher speeds than trips for the purposes that are omitted from the models. The variables in the second group have a negat ive effect in both models. The resuns indicate that, other things being equal, persons living in areas with higher population densities drive at lower speeds for both trips using all roadways and trips using limi ted-access highways. Similarly, peak trips have l owe r speeds than off-peak trips. The variables in the third group have a positive effect in the model for all roadways, but have no effect in the model for limited-access highways. The resuHs indicate that, other things being equal persons with more than a high school education drive at higher speeds than those with less education for all roadways, but at similar speeds on limited-access highways. The size of an urbanized area increases the speeds for trips using all roadways, but has no effect for trips using limited-access highways Since limited-access highways generally have higher speeds than local roadways, the positive relationsh ip between the size of an urbanized area and the speeds for trips using all roadways may imply that trips in la rger urbanized areas are more likely to use limited-access highways In fact, the analysis of dri v ing on limited -access highways in the next section confinns this implication Similarly, night trips have higher speeds than day-time trips on all roadways, but have similar speeds on limited-access highways; and work trips on all roadways have higher speeds tha n trips for those purposes that are omitted from the models, but have similar speeds on limited-access highways. Also, carpool trips have higher speeds than single-occupant trips on all roadways, but have similar speeds on limited -access highways. It is reasonable that carpool trips have higher speeds than single-occupant trips on all roadways because carpool trips may be more likely to use limited -access highways The variables in the last group have a negat i ve effect in the model for all roadways, but have no effect in the model for l imi ted-access highways. The r esults indicate that. other things be ing equal, persons living in central cities drive at lowe r speeds than those liv ing outside central cities for all roadways, but drive at similar speeds on l imited-access highways. Similarly, persons in the North East or North Central regions drive at lower speeds than those in the West on all roadways, but drive at similar speeds on limited-access highways. Also shopping trips and trips for other family or personal business have lower speeds than trips for the omitted trip purposes on all roadways, but have similar speeds on limited-access highways. LIMITED-ACCESS HIGHWAYS This section examines the effects of age on the elderly's choice of driving on limi ted access highways. It is un<:lear, ai the outset, how age may affect the elderty's use of limited access h ighways Limited-access highways have the lowest fatal Cfashes per mile driven. But they are also likely to have higher in j ury risks from crashes due to the high speeds. As 27

PAGE 34

discussed in Chapter 1, however driving on l imited -access highways is one of the commonly mentioned conditions that the elderly find difficult. The percent of vehicle miles driven on l imited-access highways is first tabulated by driver age group and trip purpose. Legit analysis is then used to isolate the effects of age on the elderly's probability of driving on limited-access highways. TABULATION Table 5.4 tabulates the percent of vehicle miles driven on limited-access highways by driver age g rou p and trip purpose. The elderly drive 21 percent of their miles on limi ted-acces s highways for wort< trips and 15 percent for non-wort< trips. The mid-aged drive 28 percent of their miles on limited -access high ways for wort< trips and 26 percent for non-wort< trips. The young drive 22 percent of their miles on limited-access highways for wort< trips and 24 percent for non-wort< trips. Table 5.4 P ercent of miles oriven on limi tee -access h ighways by oriver age group Driver Age Group All T rips Work Trips Non-Work Trips A l l 25.5% 27.2% 24.6% Young l 1 5 .3% 20.7% 14.7% Source: CalculateO from the Travel Day file. The 1990 NPT$ ranoomly sel ects a pri vate -vehi c l e trip for each respondent ( i f anyl. and breaks oan its o istance by roaoway class i fication. REGRESSION This regression analysis i s similar to that for dri v ing at night or during peak hours. The dependent variable is binary, indicat i ng whether a vehicle trip uses any limited-access highways. The legit model is used along with the maximum likelihood method for estimation. Two models are est imat ed in order to examine how controlling for speed affects the elderly's choice of driving on limited-access highways. The results are shown in Table 5 .5. Model 1 includes speed; Model 2 does not include speed. The results in both models indicate that, other things being equal, the elderly are less likely to drive on limited-access highways t han the mid-aged. The coefficients of the elderly dummy variable are -0.5618 in Model1 and -0.7364 in Model2 and both differ from zero at the 0.1 percent leve l Thus, when speed is not held constant (Model 2), the elderly's odds ratio is 52 percent lower t han the mid-aged's odds ratio of driving on limited-ac cess highways When speed is also held constant (Model 1 ), the elderly's odds ratio is 49 percent lowe r than the mid28

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Table 5.5 L egit analys i s o f dri ving on limited -access h i g hways Model 1 Model 2 Explanatory V a r ia b les Coeffic i e nts x2-Sta tistics Coefficie nt s x'-Statisti c s Personal Characteristics Age>-65 -0.5618 2 6 .2 3 0 .7364 48.24 Age< -0.2785 1 8.32 -0.1686 7 .32" Male 0.2960 42.38 0 .3366 59.42 Educatf on>h igh school 0.254 7 30.36 0.2979 44.91 W orker 0 .0927 1. 73' 0.1501 4.90' House h old Character i stics White -0.0628 0.35 0. 0075 o o r S lack 0 .061 6 0 22' 0 .1107 0.79' H i spanic 0 .0996 073' 0 .0809 0.53' Income category -0.001 3 4.43 -0.0014 5 .6 5' L ocat i o n Character istics Central c i ty 0 .0387 0 .65' -0.0772 2 .79. U rbanized -area size 0 .1361 60.15 0.1254 55.18 Popula tion density 0 0007 5.1 4' -0.0001 o .oo North East 0.4725 5 4 .3 4 -0.495 1 64.83 NOrth Central 0 .4926 54.44 -0.4891 58.23 South -0.2388 13.11 -0.2237 1 2 .54 Trio Characteristics Dark -0.0411 0 .60" 0.0361 0 5 1 Peak hours -0.0068 1 4 .36 -0.0067 1 5.06 weekend 0 .0632 1.27' -0.0341 0 .40' Winter 0 .0892 2.96. 0.0247 0.25' Carpoo l 0.1059 3.75 0.1554 8 .81 Work-related 0.1948 3.88' 0.2247 5.58' Shopping 0 .5261 22.96 -0.5916 31.33 Other fam i ly/personal -0.1891 3.22. 0 .1657 2.68' Medica l 0.0477 o.o5 0.0947 0.20" Visit ing friends/ relatives 0 .0160 0.02" 0 1275 1.39' Other social/recreational 0 .0965 0. 77' -0.0288 0 .07' Speed 0 .0430 829.04 3.!177 227.49 -1.6083 70.88 x' -Statisti c 1543 653 Mean of dependent variab l e 1 2 .984 12,999 Numbe r of observat ions 3.095 3.100 Source: Estimated f rom the sample of trips f or which d i stances are b roken down by roadway c lassifica t i on. Whether a c o eff i cien t d i ffe r s from zero i s labeled as follows: sign i f ica n t at th e 5 percent l e vel: sign i f icant at the 1 0 percent level : ins i g nif i c ant a t the 1 0 percent l e vel: other s s i g nifican t at t h e 0.1 percent level 29

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aged's odds ratio of driv ing on lim i ted-access highways. So, the elderly's odds ratio of driving on limited-access highways decreases slightly (from 52 to 49 percent) when speed is controlled This sl ight decrease seems to ind i cate that the elderly avoid driving on limned-access highways mainly for reasons other than high speeds. The other variables are organized i nto three groups for interpretation. The first group includes variables whose coefficients differ from zero at the 10 percent level in both models. The results indicate that, other things being equal, males are more likely to drive on limited-access highways than females; persons with mo r e than a high school education are more likely to drive on limited-access highways that those with less ed u cation; the probabi l ity of driving on limned access highways increases with an increase in the size of an urbanized area; li mited access highways are more likely to be used for carpool trips than for non-carpool trips ; l imited-access highways are more l i kely to be used for worl
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assumes that the elderly value comfort or prestige more than others, one may argue that the elderly may drive larger automobiles for these reasons rather than for their crashworthiness The IHerature however, provides no evidence that the elderly value comfort or prestige more than others Also, the fact that elderly drivers take trips that are shorter in distance, as shown in Chapter 3, suggests that the comfort of an automobile is less important for the elderly than for others. The 1990 NPTS associates each vehicle used on the travel day wHh a main driver This association allows one to li nk the characteristics of the main drivers wHh the attributes of the vehicles that they drive The 1990 NPTS measures vehicle size according to the National Accident Sampling System The size of an automobile is based on Hs wheelbase length and is coded on a scale from one to six For example, the size of a Ford Escort Is one and the size of a Toyota Camry is three. Only automobiles are included in the analysis Non-household owned automobiles are excluded because they cannot be related to household attributes of the main drivers. The following analysis starts with a tabulation of automobile size by age group of the main drivers and labor force participation Regression is then used to isolate the effects of age on the size of automobiles that the elderly drive TABULA nON Table 5.6 tabulates the average size of automobiles by age group of the main drivers and labor force participation For persons not in the labo r f orce the average sizes ofthe automobiles they drive are 3.16 for the elderly, 2.65 for the mid-aged, 2.52 for the young and 2.66 for all For those in the labor force, the average sizes are 2.90 for the elderly 2.61 for the mid-aged 2.35 for the young, and 2.56 for all. Table 5.6 Average size of a u tomobiles b y age group of main dr ivers Driver Age Group All Drivers In LabOr Force Not in Labor Force All 2.68 2.58 2.88 Young 2.42 2 .35 2 .52 Mid-Aged (25l 3.1 2 2 90 3.16 Source: Calcul ated from the Vehicle and Person flles as the weighted average of automobi le sizes The size of an automobi l e is based on its whee lbase length. and is on a scale from one to six 31

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REGRESSION The dependent variab l e is the size of an automobile measured on a scale fro m one to six Unlike the regression analyses so far where the unit of observation is individual drivers or vehicle trips the of observation here is individual automobiles. This analysis is similar, howeve r to those for the distance and speed of vehicle trips in that the standard linear regression model i n equation (1) is used along with the weighted least squares method for estimat i on. The results are shown in Table 5 .7. Two mode l s are estimated. Model1 inc l udes a set of personal, household and location characteristics of the main drivers. In addition to these characteristics, Mode l 2 also includes two veh i cle attributes: vehicle age and import status (whether a vehicle i s foreign-made) The resu l ts i ndicate that the coefficients of the e l derly dummy variable are 0 4039 in Mode l 1 and 0.257 4 i n Model 2 and both differ from zero at the 0 .01 percent level Thus other things being equal, the elderly drive larger automob i les than the mid-aged The other explanatory va ri ab l es are organ i zed i nto three groups for interpretation The first group includes variables whose coefficients differ from zero at the 10 percent level in both models The results indicate that other things be i ng equal, the young drive smaller automobiles than the mid-aged ; persons with more than a hig h school education drive smaller automobiles than those with less education; persons i n the labor force drive smaller automobiles than those not in the labor force ; the size of an automobile increases with an increase in household income, but decreases an increase i n the size of an urbanized area; and persons in the South drive larger automobiles than those in the West. The second group includes variables whose coefficients do not d i ffer from zero at the 10 percent level i n either model. The resuHs i ndicate that. other things being equal, living in central does not affect the size o f an automobile one drives and persons in the South East drive automobiles that are as large as those dr i ven by persons in the West. The third group includes variables whose statistical significance changes between the two models. The resuHs i ndicate that other thi n gs being equa l males are shown to drive larger automobiles than females when vehicle age and i mport status are not held constant (Model 1 ). But once vehicle age and import status are held constant (Model 2) males drive automob i les that are the same size as t hose driven by females Similar changes in statistical significance are also observed for Whites, B l acks household s i ze and persons liv i ng in the North Central region. On the other hand, when vehicle age and import status are not he l d constant (Model 1 ), Hispanics are shown to drive automob iles that are the same size as those driven by non Hispan l cs Once veh i cle age and import status are given (Model 2), however. Hispanics are shown to drive smaller automobiles. Two qualifications are i n order First, these models d o not include own i ng and operating cos ( s as an exp l anatory variable, though there i s no r eason to believe that includ i ng such a cost variable would necessarily change the results. It i s possible to estimate these costs using other sources the information on vehic l e make and model 3 However est i mating these costs would require addrtional resources and is beyond the scope of th i s study. 32

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Table 5.7 W e ighted r e g ressio n of a utomobil e size Hodel 1 Hod el 2 Explan atory Var iables Coeff ic i ents t -Statistics Coefficie nt s t Statistics Person al Character! sties Age>6 5 0 .4039 9 .94 0.2574 7.05 Age< .3380 11.71 0.2579 9.95 M ale 0.0722 3.4 0 -0.0019 0.10 E d ucat ion>high school 0 .2009 -8.85 .0618 .01 W orker 0.2091 -7.67 .1579 -6.46 Househ old Characteri stics Whi t e 0.13 2 1 2 .90' 0.0162 0.40 B lack 0.1493 2.72' 0.0 601 1.22" Hisp anic 0 .0693 -1. 4 1 -0.1536 3 49" Income category 0. 0072 2.74 0.0248 1 0.46 Househ old s ize 0 .0493 6 .15 0.0104 1.45 loca t io n Characteri stics Cen tra l city 0.0153 0.65 0.0114 o.55' Urbanized-area size 0286 3. 40" 0.0146 1.94' Population d ensity .0003 -0. 20' 0.001 9 1.20' North East 0.0404 1.2 5' 0 .0022 -o.o8 Nor th Centra 1 0.1 467 4.79 0 .0325 1.16' Sou th 0 .1386 4 .80 0.1172 4 .50 Vehicle Charact eris t ics Vehicle age 0 .0460 24.71 s t atus -0.8733 4 0 .99 2.5012 34.95 8 4.9974 -23.89 F-Stat isti c 40 178 Mean of dependent v ariabl e 2 .56 2.56 Number o f observat ions 9 .965 9 .916 Source: Estimated from the Vehicle and Person F i les wit h the w e ight e d least squares method. Whether a coeff i c i ent d iffers frcm zer o is labeled as follows: s i gnif ican t a t th e 1 per cent level: signi f icant at the 10 percent level: ins i g n i f icant at the 1 0 percent level: others s ignificant a t th e 0.01 percent level. 33

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NUMBER OF PASSENGERS CARRIED This section examines the effects of age on the number of passengers that the elderly carry Given that the elderly show increased crash involvements per of exposure one might hypothesize that they feel less comfortable with carrying passengers than younger persons. The following analysis first tabulates the average automobile occupancy by driver age group and trip purpose. Regression is then used to iso late the effects of age on the number of passengers carried in each vehicle trip on the travel day TABULATION Table 5.8 tabu la tes the average occupancy of automobile trips by driver age group and trip purpose. The elderly's average occupancies are 1.39 for all purposes, 1.08 for wor1<. trips, and 1.41 for non-wor1<. trips The mid-aged 's average occupancies are 1.54 for all purposes 1.14 for wor1<. trips, and 1.71 for non-wor1<. trips The young's average occupancies are 1.44 for all purposes, 1.10 for wor1<. trips, and 1 56 for non-wor1<. trips Table 5.8 Average occupancy o f automobi le trips by dr iver age g roup Drive r Age Group All Trips T rips Trips All 1.51 1.1 3 1.65 Young (Age<-24) 1.44 1.10 1.56 Hid-Aged <25l 1.39 I. 08 1.41 Source: Calculated from the Travel Day File as the weighted average of occupancies of indi vidual automobil e trips on the travel day. REGRESSION The dependent variable i s the number of occupants in an automobile trip on the t ravel day. This regression analysis is similar to those for the distance and speed of vehicle trips in two ways. First, the unit of observation is individual vehicle trips. Second, the standard linear regression model in equation (1) is used along w ith the weighted least squares me t hod for estimation. This analysis differs, however, from those for the distance and speed of vehicle trips in that this analys i s includes additional variables that measure household and vehicle ownership The results are shown i n Table 5.9 The results indicate that the coefficient of the e lde rly dummy variable is -0 0558 and differs fro m zero at the 1 percent level. Thus, other things being equal, the elderly carry fewer passengers than the mid-aged 34

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Table 5.9 Weighted regression of occupancy of automobile trips Explanatory Variables Coeff ici ents tSta tistics Personal Characterist ics Age>-65 0. 0558 .98" Age< 0.1087 .52 M al e 0 0 136 !.59" Education>high school 0.0007 0 .07" .0543 4.58 Househ o l d C h aracte ristics White -0.0232 -1.18" 0583 .48 H ispanic 0.0121 0 .58" Single .2625 -19.40 # o l d childre n 0.1487 48.21 # vehicles 0.0635 .52 lnccwne c ategory .0038 -3.45" Locatjon Characterist ics Central city 0 .0084 0.90" Urbanized-area size .0132 .95 Population den sity 0.0034 4.24 N orth East .0588 4 3 2 North Central .0935 -7.65 South .0543 4.62 Gasoline Price 0.0010 1.10" Trjp C h a ra cteristics 0 0 437 4.13 hours .0185 04. end 0.1601 16.05 Winter -0.0101 I. 04" Distance 0 .0031 12.10 .5085 .19 Shopping -0.1607 8 .54 other fami ly/personal -0.0305 66. Medical 0.1720 -3.98 Visiting frien ds/ r elatives -0.1067 -5.03 other social/recreational 0.2463 12.14 1.6987 18.75 F Stat istic 278 Mean of dependent variabl e 1.50 Number o f obser vations 37.097 Source: Estimated from the Travel Day F i l e with the weighted l east squares method. a c oefficient d i ffers from zero is labeled as follows: signif i cant at the I percent level: significant at the 10 percent leve l : insignificant a t the 1 0 percent lev el: others signif icant at the 0.01 percent l e v e l 35

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The other variables are interpreted by category of characteristics. Among the personal characteristics, the young carry fewer passengers than the mid-aged and persons in the labor force carry fewer passengers than those not in the labor force. In addition, males carry just as many passengers as females. Among the household characteristics, automobile occupancy decreases with an increase in household income and vehicle ownership; persons from household with more children between the ages of 5 and 22 years carry more passengers; persons from single-resident households carry fewer passengers than those from households; and Blacks carry fewer passengers than non-Blacks. Also, Whites carry as many passengers as those who are neither White nor Black; and Hispanics carry as few passengers as non-Hispanics. Among the location characteristics, automobile occupancy increases with an increase in population density. but decreases with an increase in the size of an urbanized area; automobile occupancy is lower in the other census regions than in the West. In addition, living in central cities does not affect automobile occupancy Gasoline price, as measured in this analysis, has a positive but statistically insignificant effect on automobile occupancy. Among the trip characteristics, night trips have higher occupancies than day trips; weekend trips have higher occupancies than weekday trips ; and long distance trips have higher occupancies than short distance trips. In addition. trips for other social or recreational purposes have higher occupancies than trips for those purposes that are omitted from the model; and trips for the other remaining purposes inc luded in the model (work-related, shopping, other family/personal business, medical, and visiting friends/relatives) have lowe r occupancies than trips for the omitted purposes The omitted purposes include trips for school or church, trips for vacation, trips for pleasure driving and trips for other purposes. 36

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Chapter 6 SUMMARY AND POLICY IMPLICATIONS This report has examined the effects of age on six driving habits of the elderly (persons age 65 years or older) This chapter summarizes the main results and discusses the im plications of these resutts to policymaking in areas conceming the mobility and traffic safety of the elderly. SUMMARY Eld erly drivers show an increased effort of self-protection in their driving habits relative to mid-aged drivers (persons between the ages of 25 and 64 years). Elderly drivers not only reduce daily driving exposure avoid driving at night, avoid driving during peak hours, and avoid driving on lim ited access highways, but a lso drive at lower speeds drive larg er automobiles, and carry fewer passengers. The following summarizes the resu lts for each of the six driving habits examined Daily Driving Exposure. The elderly reduce t heir daily driving exposure by reducing not the frequency but the distance of vehicle tri ps. The elderly drive fewer vehicle miles than the mid-aged. They take as many vehicle trips as the mid-aged, but their vehicle trips are shorter in distance than those taken by the mid-aged. Driving By Time of Day The elderly are less l ikely to drive at night and during peak hours than the mid-aged. In addition, the e lderly are lesse r likely to drive at night than to drive during peak hours. This i s consistent with the fact that the elderly find driving at night more problematic than driving during peak hours Driving By Roadway Type. The elderly are less likely to drive on limi ted-access-highways than the mid-aged. This avoidance behavior by the elderly can be due to many characterist ics of limi ted-access-highways, such as high speeds When speed Is held constant, however, the elderly still are found to be less likely to drive on limited-access highways. In addition, the elderly's like l ihood of driving on limited-access-highways decreases only slightly when speed is held constant. This slight decrease seems to suggest that the elderly avoid driving on limited -access-high ways mainly due to characteristics of limited-access-highways other than high speeds. Driving Speed. The elderly drive at lowe r speeds than the mid-aged. They drive about 4 miles per hour (mph) slower than the mid-aged for all trips This is either because the elderly are more likely to drive on roadways with lower speed limits or because they drive slower on roadways with the same speed limits The evidence indicates that both 37

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possibilities occur with the elderly. \Nhen only vehicle trips that use limed-access highways are considered, the elderly are found to drive about 4 mph slower than the mid-aged. As indicated earlier, the elderly also are less likely to drive on limed-access-highways. Automobile Size The elderly drive larger automobiles than the mid-aged. \Nhen the size of an automobile is measured by wheelbase size on a scale from one to six, the average size of automobiles driven by the elderly is 0.40 smaller then that by the mid-aged when automobile age and import status are not held constant and is 0.26 smaller when automobile age and import status are he ld constant. Number of Passengers Carried. The elderly carry fewer passengers than the mid-aged. In fact, the elderly carry an average number of passengers t hat is about 0.05 lower than the mid-aged. These differences in the driving habits between the elderly and mid-aged reflect the marginal effects of age difference between the two groups. These differences do not reflect any effects of the differences between the two groups in other personal, household, locatio n, an d trip characteristics that are held constant in this study. POLICY 1M PLICA TIONS Despite their increased effort of self-protection in their driving haMs, as summarized above, the elderly still show a higher risk of crash and injury per unit of exposure than t he mid aged.' \Nhen the elderly adjust thei r driving habrts, they consider the ris ks they face, but not the extemal risks they impo se on others when they drive. If the elderly are forced to adjust their driving habrts further to offset the external risks of their driving, their risk of crash and injury would be reduced and society as a whole would be better off. Any further adjustment in the e lderly's driving habits, however, is likely to make the elderly worse off due to reduced mobility. The challenge to policy-making is to balance these consequences of any policy conceming the mobility and traffic safety of the elderly. The following discusses f our existing policy options. Removing Hazardous Elderly Drivers from Roadways! Removing elderly drivers through the use of stricte r licensing laws is controversial. First, the removed drivers are forced to pay a large price-loss of automobile mobility. Second elderly drivers have the lowest severe crash involvement per driver. If the purpose is to reduce the maximum number of severe crashes per removed driver, then removing younger drivers would be far more effective than removing e lderly drivers. Third, the physical and cngnitive a(:)ilities vary widely among the elderly. Forth, such removal has the appearance of discriminating against elderly drivers. As a result, the higher the proportion of elderty drivers that a state has, the harder to implement such an option. The best example is Florida where 38

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the elderly population as a share of the total population is the highest in the nation. Three attempts by Florida's leg islature to pass stricter licensing laws for elderly drivers have failed in the past several years. Making Alternatives to Driving Available. This option accommodates the option of removing elderly drivers from roadways. Alternatives to driving include walking, public transit, specialized transportation. and the use of taxis. As more elderly persons live in suburlbs where t he population density is low, these aHematives become less feasible. Walking is difficult for elder1y persons in low density areas, and it is extremely costly to expand public transit for the elderly in these areas. Expanding specialized transportation to low density areas is also expensive Subsidizing the use of taxis may be more expensive than specialized transportation. Improving Vehicle and Roadway Design and Operation. 5 This option attempts to accommodate the reduced physical and cognitive abilities of elderly drivers. There is, however, strong evidence that drivers become more risk-taking when the driving environment becomes safer There i s no reason to believe that elderly drivers do not have such a behavior. This behavior would off-set many of the in tende d benefits of Improving vehicle and roadway design and operation. Re-Educating Elderly Drivers.' Re-educating elderly drivers would be an appropriate policy if elderly drivers were not fully aware of their reduced cognitive and physical abilities and the conse q uences to their traffic safety As the number of elder1y drivers continues to grow, the welfare of the society as a whole becomes increasingly dependent upon the mobility and traffic safety of elderly drivers. While this study has implications to po li cy-making, po licy recommendation is beyond the scope of this report. Future research needs to examine the impacts of existing policies, as well as to develop new policy options that would better balance the effects on the elderly and society as a whole 39

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ENDNOTES CHAPTER 1 1. Elderly is defined as age 65 years or older. This is the most commonly used definijion in the literature on the mobility and safety of elderly persons 2. Federal Highway Administration, 1990 Nationwide Personal Transportation SuNey: Users Guide for the Public Use Tapes, Advance Copy (Washington, D.C.: U.S. Department of Transportation 1991 ). 3. Summary of Findings and Recommendations : Highway Mobility and Safety of Older Drivers and Pedestrians (Washington, D.C.: Highway Users Federation for Safety and Mobilijy, 1985); Tra nsportation Research Board, "Executive Summary," in Transportation in an Aging Society: Improving the Mobility and Safety of Older Persons Vol. 1 Committee Report and Recommendations (Washington D.C.: Nationa l Research Council 1988); and Conference on Research and Development Needed to Improve Safety and Mobility of Older Drivers (Washington D .C.: National Highway Traffic Safety Administration, 1990?). 4. The TRB effort and Congressional request resuHed in a two-volume report by TRB, Transportation in an Aging Society: Improving Mob ility and Safety for Older Persons, Special Report 218, Vol. 1 : Committee Report and Recommendations, Vol. 2: Technical Papers (Washington, D.C.: National Research Council, 1988) 5 The result is a report BY THE U S Department of Transportation, Older Driver Pilot Program: Report of the Secretary of Transportation to the United States Congress (Washington, D.C.: Federal Highway Administration, 1990). 6. For example, Max Israelite, Take Away My License: I Would Rather Stop Driving Too Soon Than Too Late (Elderly Automobile Drivers) in Newsweek (May 9, 1994): 11; Joan E Rigdon, "Car Trouble: Older Drivers Pose Growing Risk On Roads As Their Numbers Rise; They Crash More Than Many, Yet Taking Away Wheels Leads To Isolation. Anger; A Man Runs Over His Wife" in Wall Street Journal (October 29 1993): A1; Lisa J Moore "Drive on Miss Daisy (older automobile drivers)" in U.S. News & World Report (June 22, 1992) : 8384; Alan L. Otten, "Older Drivers Appear Safer But More Frail (National lnstijute On Aging Study Reveals Older Drivers More Li kely To Die In Auto Accidents Than Younger Drivers)" in Wall Street Journal (June 1, 1992) : 81; "Safety And The Older Driver: When Difficult Issues Collide (Federal And State Authorities Struggle To Identify Aged Drivers Who Pose A Hazard While Not Discriminating Against Those Who Do Not)" in New Yorl< Times (May 4, 1992): A1; Sandy Rovner, "Driv ing Difficulties Increase With Age" in Washington Post 40

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(October 30, 1990): WH16; and James Camey "Can A Drive r Be Too Old? Fender Benders And Fatalities Raise Fears Over Elderly Motorists" in Times (January 16, 1989): 28. 7. U.S. Federal Highway Administration (FHWA), Highway Statistics, 1990 (Washington, D.C.: FHWA, 1991 ), Table DL-20: and FHWA, Highway Statistics, Summary to 1985 (Washington, D.C.: FHWA, 1987), Tab le DL-220. 8. U.S. Bureau of the Census, Statistical Abstract of the United States, 1992 (Washington, D.C.: U.S. Government Printing Office, 1992), Tab le 14. 9. FHWA, Highway Statistics, 1990, Table DL-20; and FHWA, Highway Statistics, Summary to 1985, Table DL-220. 10. Ruth H. Asin, Characteristics of 1977 Licensed Drivers and Their Travel: Report 1, 1977 NPTS (Washington, D.C.: FHWA, 1980), Table 16; and Ezio C. Cerrelli, Crash Data and Rates for Age-Sex Groups of Drivers, 1990 (Washington, D C.: National Center for Statistics & Analysis, 1992), Table C 11. The elderly population is expected to reach 20 percent of all persons by the year 2020, according to Census Bureau, Projections of the Population by Age, Sex, and Race for the United States, 1983-2080 (Washington, D.C.: Government Printing Office, 1984), No. 952, Series P-25, cited by TRB, Transportation in an Aging Society, Vol. 1: 22. In 1990, the elderly population was 12.5 percent of all persons while the number of elderly drivers was 13.3 percent of all drivers. 12. Finn J0rgensen and John Polak, "The Effect of Personal Characteristics on Drivers' Speed Selection," Journal of Transport Economics and Policy, 27 (September 1993): 237-252. 13. TRB, Transportation in an Aging Society, Vol. 1: 61, 72. 14. Ibid.: 39-40. 15 J. Peter Rothe The Safety of Elderly Drivers : Yesterday's Young in Today's Traffic (New Brunswick: Transaction Publishers, 1990), p 64. 16. S.J. Flint, K W Smith, and D G. Rossi, "An Evaluation of Mature Driver Performance," paper presented at the 14th International Forur: em T;affic Records Systems, San Diego (1988), cited by J. Peter Rothe, The Safety of Elderly Drivers, 127. 4 1

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17. P.A. Bra l nn Safety and Mobility Issues in Licensing and Education of Older Drivers (Washington, D C.: NHTSA U.S De p artment of Transp o rtation, 1980), by Sandra Rosenb l oom, "The Nee d s of the Elderly i n Transportation in an Aging Society : Improving Mobility and Safety for Older Persons Special Report 218, Vo l 2. Technical Papers (Wash ington D C : National Research Council 1988), 40 18. R. R i sser and C Cha l oupka, "Elderly Drivers : R i sks an d Th ei r Causes," in Proceedings of the Second lntemational Conference on Road Safety, e d by J.A. Rolhengatter and R.A. de Bru in (Assen, Netherlands: Van Gorcum 1987), cited by Sandra Rosenbloom, 'The Needs of the Elde rl y," 40. CHAPTER 3 1. G.S. Maddala, Limited Dependent and Qualitative Variables in Econometrics. Econometric Society Monographs, No 3 (Cambridge, Mass : Cam b ridge Un i vers i ty P r ess, 1983) : 149165 2. User's Gu ide, Version 6, Fourth Edition (Cary, NC : SAS I ns t i tute I nc. 1989): 1005-6 3 Bureau of Census Statistical Abstra ct, 1991 (Washington, D.C : U.S. Department of Commerce, 1992) No 762 RefineriReseller Sa les Price of Motor Gaso l ine by Grade and State: 1989 to 199 1 ; and No 998 State Gaso l ine Tax Rates, 1990 and 1991, and Motor Fuel Tax Receipts, 1990 4 The SAS procedure used for est imation, LIF EREG does not report the log l ikelihood at ze r o (i.e when all explanatory variables are excluded) 5. For more on the interpretat i on of ToM models see John F. McDonald and Robert A. Moffitt, 'The Uses of Tobit Ana l ysis," The Rev iew of Economics and Statistics 62 (1980): 318-321. 6 Rosenbloom, 'The Mob i lity Needs of the Elderly Vo l. 2: 33-34 42

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CHAPTER 5 1. FHWA, User Gu ide to the 1990 Nationwide Personal Transportation SuiVey, Append i x J : Nationa l Accident Sampling System Vehicle Make and Mode l Coding Dictionary (Wash i ngton D.C : Department of Transportation 1g9 1 ) 2 A more appropriate too l wou l d be grouped data reg r ession or ordered probit regression (\Nilliam H. Green, Econometric Analysis, New York : MacMillian Pub l ishing Company 1990) 3 Kenneth Tra i n Qualitative Choice Analysis: Theory Econometrics, and an Application to Automobile Demand, M I T Press Series i n Transportation Studies, Marvin L. Manheim, ed. (Cambridge, Mass.: M.I.T. Press, 1 986) : 143-144 CHAPTER 6 1. See Chapter 1 2 TRB Transportation in an Aging Soc iety, Vol. 1: 76-103 3 A D Burch "Bill Targets O l d Young For Added Driving Tests" i n The Orlando Sentinel (March 3, 1994) : C-1. 4 TRB. Transportation in an Aging Society Vol 1 : 76-103 5 Ibid 6 Sam Peltzman "The Effects of Autom o bile Safety Regu l ation Journal of Political Economy, 83 (June 1975): 677 725 7. TRB. Transportation in an Aging Society, Vo l. 1: 761 03. 43


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