Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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The health status effects are positive as expected, showing that better health status permits more driving. However, these effects are highly significant statistically for men only in the 70-74 and 75-79 age groups (marginally significant for the 80-84 group) and for women only in the 80-84 and 85+ age groups (with a marginally significant effect for the 75- 79 group). It seems as if this measure of health status only has a material effect among the older women, while its effect seems restricted to men in the middle periods of old age. The magnitudes of the significant effects for both men and women are rather large, with elasticities of 1.23 and 0.52 for the men 70-74 and 75-79 and 0.58 and 1.92 for the women 80-84 and 85+. Having other drivers in an elderly person’s household generally has a stronger dampening effect on VMT for women than for men, but no clear pattern of effect by age emerges, suggesting that the impact of having other drivers in the household on the amount of driving does not change by age. However, that gender effect is reversed in the 85+ age group where there is no identifiable effect on women and the dampening effect on men is substantial, with its coefficient of -0.81 being larger than in any other age group. Being in the work force generally has a stronger positive effect on women’s VMT than on men’s, but as with the effect of other driver, there is no clear age pattern among the magnitudes of that effect. For 75-79 year olds, the effect is stronger for men than for women. Otherwise, the effects are always larger for females than males, and the differences are statistically significant for all age groups except the 85+ group, for which both the male and female effects are not statistically significant. The increases in VMT captured by our time trend variable, which is a proxy for other differential changes between cohort groups, are about the same for men and women among the 65-69 and 75-79 age groups. In other cases, these effects are roughly twice as large for men at any age. GM Project G.6 7 - 8 October 2000
7.3 PROJECTING VMT For projection of VMT, we used our previously developed model (2) based on data from 1977, 1983, and 1990, and substituted the actual means of 1995 VMT for the constant term in the regression equation. This substitution was done in order to ensure our 2000 projections did not represent an unrealistically large departure from actual 1995 levels. We used the remaining regression coefficients to project changes in VMT according to projected percent changes in the values of the independent variables. The logarithms of VMT, income and health status were used in the regression, yielding regression coefficients with the forms of elasticities: the percent change in the dependent variable (VMT) per one-percent change in the independent variable (income and health status). Employment status and other driver were projected as changes in the percentages of the population in each age/gender group in the labor force (essentially a labor force participation rate for employment status) and living in a household with other drivers— changes relative to the group percentages in 1995. The regression coefficients of these variables, given the log transformation of VMT, yielded percent change in VMT caused by one-percent changes in these two variables. This projection is essentially an add-to-base method. In the absence of changes in the independent variables and exogenous growth determining annual VMT per driver, VMT would continue into the future at its 1995 rate. Only changes in those determinants of VMT, or any added time trend, will affect VMT in future years. As explained in Section 7.2, the final VMT regressions generally were very good in terms of showing significant relationships of the expected signs of independent variables, and even explanation of the variance in VMT, considering the individual survey character of the data. Nevertheless, some of the coefficients for individual variables for specific age/gender groups showed non-significant relationships or relationships that were significant but of unusual magnitude. Rather than project VMT using these coefficients mechanically, we altered some of the coefficient values, which can be identified in the “Adj ß” column of Table 7.1. Where possible, we used information from regressions estimated under different specifications when searching for a significant coefficient value to use in place of a non- GM Project G.6 7 - 9 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
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 90 and 91: more likely to be drivers if there
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 106 and 107: significant one in the final set of
Page 108 and 109: empirically derived regression coef
Page 110 and 111: GM Project G.6 7 - 14 October 2000
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
Page 124 and 125: projection the asymptotic behavior
Page 130 and 131: percentages of people driving, and
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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Page 140 and 141: Table 9.2. Sensitivity of 2025 Proj
Page 142 and 143: Age group Table 9.6. Sensitivity of
Page 144 and 145: 9.3.1 The Effect of Time on VMT Pro
Page 146 and 147: Figure 9.4. Projected VMT, Women (U
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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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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