Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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more likely to be drivers if there are other drivers in the household, but men in the 75-79 or 85+ age groups are less likely to drive under those circumstances. There is effectively no influence of other drivers on 80-84 year old men. Women, on the other hand, are always less likely to continue driving when there are other drivers in the household, and the strength of this effect has a general upward trend with age. Overall, the negative relationship between having other drivers and continuing to drive for women is stronger than the positive relationship for men. Being in the labor force has a strong positive effect on the probability of women driving, with a the effect roughly increasing with age. For men the effect is always positive but is statistically significant only in the middle three age groups of 70-74, 75-79, and 80-84. Except among 80-84 year olds, being in the workforce influences a woman’s decision to drive more than it does for a man. The magnitudes of the significant coefficients range from 0.23 to 0.73. The regional time trends are generally positive (significant while negative only for 65- 69 males in the northeast Census region). They also tend to be somewhat larger for women than for men and get larger for older age groups, indicating that our behavioral variables alone tend to underpredict the growth of driving among the older elderly and among women, neither of which is surprising. 6.3 PROJECTING THE PROPORTION OF OLDER POPULATIONS THAT WILL DRIVE The percentage of each age/gender group who will continue to drive is projected with the originally estimated regression equation (1). Since values of the dependent variable are constrained (the ratio cannot exceed one), it was necessary to retain the asymptotic behavior of the dependent variable imposed by the form of the equation in the projection. For projections of future years’ values of the dependent variables, the projected levels of the GM Project G.6 6 - 7 October 2000
independent variables were substituted into the logistic equation and the new values of the dependent variables calculated. Projecting with the constant growth rates estimated in the regressions of the probability of continuing to drive would have yielded absurd results. It is well known that projecting constant growth rates from a period in the past into an indefinite future frequently produces untenable results, and this problem was particularly prominent in the cases of these three limited dependent variables. In the projections of the proportion of elderly who will continue to drive, we used the time trend with an allowance for decreasing growth rates at every five-year interval in the future. The procedure we used is detailed in Appendix C. While this is a complicated set of adjustments to the time effect in the projections, it avoids arbitrarily changing what we consider to be the more basic coefficients relating income, employment status, presence of other drivers in the household, etc., to the driving choice. Furthermore, the basic adjustment to lower the pure time effect represented in the time trend coefficients had its own empirical basis, and the subsequent adjustments on individual coefficient values is based on a combination of how age affects the driving choice and patterns in the base projections themselves. Aggregate projections of the independent variables were used to project the probability of continuing to drive. The projection of household income, by age, gender, and region, was supplied by DRI’s projections in constant 1998 dollars, using their regional forecasting model. Those projections are reported in Tables B.5 and B.6 in the appendix, with further explanation in Appendix B.2.1. Health status was projected to remain at its 1995 level throughout the projection period in the base projection but was projected to grow at ¼% per year in another projection discussed in Chapter 9. The other driver variable was a dummy variable in the regression, taking a value zero or one for absence or presence of other drivers in the household. The value of this variable was projected for each population segment (age/gender, region) by taking the proportion of that population living in a household with other drivers (a fraction between zero and one) and maintaining that level throughout the projection period. Employment status was projected by extrapolating Bureau of Labor GM Project G.6 6 - 8 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
Page 40 and 41: assessments of health status) does
Page 42 and 43: 3.2.2 Impacts of Increasing Frailty
Page 44 and 45: current vision screening tests at d
Page 46 and 47: Number of Licenses (thousands) 100
Page 48 and 49: 3.3.2 Changes in Driving Habits In
Page 50 and 51: Transportation Survey (NPTS) data,
Page 52 and 53: tourist services, and “smart” s
Page 54 and 55: Specific highway enhancements to re
Page 56 and 57: 3.4.2 Other GM Project Results (Pro
Page 58 and 59: • As age increased, being struck
Page 60 and 61: men using mid-1990s data, this gend
Page 62 and 63: major contributing factor as was dr
Page 64 and 65: Total (percent) 100 80 60 40 20 0 A
Page 66 and 67: cohort behavior and conditions for
Page 68 and 69: 4.2.3 Drivers and Driving Distance
Page 70 and 71: The HRS and the AHEAD studies are c
Page 72 and 73: these data and weights. Because of
Page 74 and 75: U.S. Census Bureau (http://www.cens
Page 76 and 77: 5. OVERVIEW OF THE MODELING SYSTEM
Page 78 and 79: or acquisition costs, of the goods
Page 80 and 81: institutionalized settings, our pro
Page 82 and 83: elationships between health and the
Page 84 and 85: 6. THE PROPORTION OF THE ELDERLY PO
Page 86 and 87: Employment status represents a deri
Page 88 and 89: statistical relationship exists bet
Page 92 and 93: Statistics (BLS) projections beyond
Page 94 and 95: considerably greater proportional c
Page 96 and 97: Table 6.3 (continued) Western Men W
Page 98 and 99: Daily Person Trips/Person Figure 7.
Page 100 and 101: Second, VMT as a consumption item i
Page 102 and 103: century on which we are unable to o
Page 104 and 105: The health status effects are posit
Page 106 and 107: significant one in the final set of
Page 108 and 109: empirically derived regression coef
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Page 112 and 113: Table 7.3. Determinants of Projecte
Page 114 and 115: 8. FATAL CRASH RATES 8.1 MODELING F
Page 116 and 117: The other fatality risk concept we
Page 118 and 119: dropped. Since these measures have
Page 120 and 121: have a regular pattern over the age
Page 122 and 123: Table 8.2. Total Risk Crash Rate Re
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Page 132 and 133: 9.1.1 Computations The projection o
Page 134 and 135: Figure 9.2, the combination of thes
Page 136 and 137: Table 9.1. Sensitivity of Total Dri
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Table 9.2. Sensitivity of 2025 Proj
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Age group Table 9.6. Sensitivity of
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9.3.1 The Effect of Time on VMT Pro
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Figure 9.4. Projected VMT, Women (U
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Figure 9.6. Component Contributions
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Step 1: Introduction Step 2: Decisi
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Step 3a: Modifying Income at differ
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Clicking on one of the sheet tabs w
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10. SUMMARY, CONCLUSIONS, AND RECOM
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similarly relied on a regression mo
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trend of fatal crashes in the futur
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The base of exposure to crash risk
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population, and a slightly smaller
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Second, improvements in health stat
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projections of VMT may be high. Our
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of entry/exit, safety, security, et
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Blincoe, Lawrence. 1994. Estimating
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College of Commerce and Business Ad
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National Association of Development
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Suen, S. Ling, C. G. B. Mitchell, a
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GLOSSARY Baby boomers Persons born
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Table A.1.2. Projections of Vehicle
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Table A.1.4. Projections of Vehicle
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Table A.2.2. Projections of Drivers
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Table A.2.4. Projections of Drivers
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Table A.3.2. Elder Driver Fatality
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Table A.3.4. Elder Driver Fatality
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Table A.4.2. Projected Elderly Driv
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Table A.4.4. Projected Elderly Driv
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Table A.5.2. Projected Total Fatali
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Table B.1. Projections of Non-Insti
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Table B.3. Projections of Non-Insti
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B.2. INDEPENDENT VARIABLES PROJECTI
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Table B.5. Male DRI Income Projecti
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B.2.1.2. Projection Adjustments to
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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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Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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