Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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projections of VMT may be high. Our attempts to estimate a household production model of VMT were hampered by lack of data. This production model could help account for the fact that higher income would give people a higher cost of using their time to drive as well as directly increasing their demand for driving. It may be possible to combine data on time use from other surveys with NPTS data to better account for the dampening effect of time requirements on increasing VMT. Other, complementary approaches to estimating a household production model could examine interactions of vehicle fuel efficiency, fuel, and vehicle safety features simultaneously with the demand for VMT. 10.3.3 Additional and Improved Measures of Health Status A third area that needs further research is the construction of a measure of health status. In the present effort we developed a method to transfer structural relationships regarding the ALS construct from the NHIS survey on health to the NPTS survey data base on driving behavior. It would be useful to identify other health indicators relevant to driving behavior for such cross-data base transfer. Even more useful would be to combine questions about driving and health status directly in a single survey, but that would lead to research capabilities only well in the future. Other more comprehensive data bases that link health status with VMT, driving vs non-driving, or crash information— or any combination of the three travel behaviors— should be examined for the possibilities of behavioral modeling to further identify choice relationships. The relative frailty of older cohorts, meaning the likelihood that a crash that a younger driver would survive could kill an older driver, is lost within the age-specific crash risks estimated in our crash risk regressions and projected from those equations. Identifying the contribution of such frailty to elderly fatalities or crash risks would be empirically demanding. Such identification would require individual-level observations on crashes with information on type of crash (head-on, side, single-vehicle, multiple-vehicle), estimated speed of impact, make/model/year of vehicles of each party to the crash, and age of driver (or of each passenger killed). These information requirements might be met from some state records if not at the national level. As a policy issue such GM Project G.6 10 - 13 October 2000
frailty may be reduced by improving elderly health trends or through general technological improvements in safety equipment that can help a frail person survive a crash. 10.3.4 Comparison of Younger and Older Drivers’ Behavior This study’s finding of the importance of employment status as a determining force in elder driver fatalities points to the question of how much of this effect is a pure age effect and how much is comparable to the exposure to crash risk faced by all working drivers. This issue can be addressed to some extent with currently available information, but projection of younger fatalities into the future would require income projections for the younger age groups, which may behave quite differently from those of the elderly living between 2000 and 2025. Another issue that bears further investigation is age-specific seat belt use. The seat belt use information available for this study was population-wide. If age- and/or gender- specific seat belt use information were made available, it might be possible to study seat belt use as a choice made simultaneously with other driving choices such as VMT, driving, and crash rate. 10.3.5 Alternative Transportation Options for the Elderly As noted in Section 10.3.1, there are several uncertainties regarding mobility of the elderly in the future. The impacts of smart cars, infrastructure enhancements, and innovative safety programs and/or components can simply not be quantified at this time. However, current trends are to solve the problems of traffic congestion, air pollution, and safety through better engineering and creative approaches to public transportation rather than by building new roads. The need for alternative mobility options for the elderly is well recognized, and various concepts for making transit use more acceptable to elderly riders are being explored. Additional research is needed in the areas of public transportation as well as other alternative mobility options. For example, more information on public transit is needed (1) to identify the features that the elderly desire in a transit system (price/method of payment, ease GM Project G.6 10 - 14 October 2000
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ORNL-6963 Projecting Fatalities in
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4. ANALYSIS OF DATA SETS ..........
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GM Project G.6 vi October 2000
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LIST OF TABLES Table ES-1 Elderly D
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Table A.5.3 Projected Total Fatalit
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NASS National Automotive Sampling S
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Over the past century, the numbers
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Figure ES-1. Projected Active Drive
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Table ES-1. Elderly Driver Fatality
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1. INTRODUCTION America is graying.
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of existing data bases, including h
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various data sources and their abil
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Finally, data sets should contain,
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• Examine data sources to determi
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health care, resulting in longer li
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Age group Table 3.2. Projected Rati
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that individuals will not retire at
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analysis. Distribution of the elder
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More elderly people live in rural a
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assessments of health status) does
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3.2.2 Impacts of Increasing Frailty
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current vision screening tests at d
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Number of Licenses (thousands) 100
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3.3.2 Changes in Driving Habits In
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Transportation Survey (NPTS) data,
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tourist services, and “smart” s
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Specific highway enhancements to re
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3.4.2 Other GM Project Results (Pro
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• As age increased, being struck
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men using mid-1990s data, this gend
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major contributing factor as was dr
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Total (percent) 100 80 60 40 20 0 A
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cohort behavior and conditions for
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4.2.3 Drivers and Driving Distance
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The HRS and the AHEAD studies are c
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these data and weights. Because of
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U.S. Census Bureau (http://www.cens
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5. OVERVIEW OF THE MODELING SYSTEM
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or acquisition costs, of the goods
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institutionalized settings, our pro
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elationships between health and the
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6. THE PROPORTION OF THE ELDERLY PO
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Employment status represents a deri
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statistical relationship exists bet
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more likely to be drivers if there
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Statistics (BLS) projections beyond
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considerably greater proportional c
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Table 6.3 (continued) Western Men W
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Daily Person Trips/Person Figure 7.
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Second, VMT as a consumption item i
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century on which we are unable to o
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The health status effects are posit
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significant one in the final set of
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empirically derived regression coef
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GM Project G.6 7 - 14 October 2000
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Table 7.3. Determinants of Projecte
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8. FATAL CRASH RATES 8.1 MODELING F
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The other fatality risk concept we
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Page 120 and 121: have a regular pattern over the age
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Page 124 and 125: projection the asymptotic behavior
Page 126 and 127: GM Project G.6 8 - 13 October 2000
Page 128 and 129: GM Project G.6 8 - 15 October 2000
Page 130 and 131: percentages of people driving, and
Page 132 and 133: 9.1.1 Computations The projection o
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Page 136 and 137: Table 9.1. Sensitivity of Total Dri
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Page 142 and 143: Age group Table 9.6. Sensitivity of
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Page 148 and 149: Figure 9.6. Component Contributions
Page 150 and 151: Step 1: Introduction Step 2: Decisi
Page 152 and 153: Step 3a: Modifying Income at differ
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Page 156 and 157: 10. SUMMARY, CONCLUSIONS, AND RECOM
Page 158 and 159: similarly relied on a regression mo
Page 160 and 161: trend of fatal crashes in the futur
Page 162 and 163: The base of exposure to crash risk
Page 164 and 165: population, and a slightly smaller
Page 166 and 167: Second, improvements in health stat
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Page 172 and 173: Blincoe, Lawrence. 1994. Estimating
Page 174 and 175: College of Commerce and Business Ad
Page 176 and 177: National Association of Development
Page 178 and 179: Suen, S. Ling, C. G. B. Mitchell, a
Page 180 and 181: GLOSSARY Baby boomers Persons born
Page 182 and 183: Table A.1.2. Projections of Vehicle
Page 184 and 185: Table A.1.4. Projections of Vehicle
Page 186 and 187: Table A.2.2. Projections of Drivers
Page 188 and 189: Table A.2.4. Projections of Drivers
Page 190 and 191: Table A.3.2. Elder Driver Fatality
Page 192 and 193: Table A.3.4. Elder Driver Fatality
Page 194 and 195: Table A.4.2. Projected Elderly Driv
Page 196 and 197: Table A.4.4. Projected Elderly Driv
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Page 206 and 207: Table B.1. Projections of Non-Insti
Page 208 and 209: Table B.3. Projections of Non-Insti
Page 210 and 211: B.2. INDEPENDENT VARIABLES PROJECTI
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Page 216 and 217: B.2.5 Seatbelt Use Our projections
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C.3. DECREASING GROWTH RATES OF DRI
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GM Project G.6 C - 4 October 2000 T
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