Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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considerably greater proportional changes, with national averages in 2025 ranging from 28% higher (65-69 women) than 1995 levels to 113% (85+). The greatest proportional increases occur in the 85+ women in all regions, reaching 122% and 142% increases in the Northeast and South respectively. In 1995, 65.1% of women 65-69 were drivers nationally, with that percentage rising to 83.6% by 2025. In the West, 92.9% of women in that age group are projected to drive by 2025, but only 64.1% in the Northeast (which also had the smallest percent increase over the period). Only 19.2% of women 85+ drove in 1995, and nearly 41.1% are projected to drive by 2025, with the highest proportion in the Midwest at 52.5% and the lowest in the Northeast at 22.2%. Tables 6.2 and 6.3 show the national and regional contributions of growth in income and labor force participation (LFP) to the projected growth in the percentage of drivers between 1995 and 2025. Income is the predominant, identifiable social determinant for men, but the influence of the time trend is dominant for women of all ages. The effect of income declines steadily by age group for men, and generally but not uniformly for women. The effect of LFP growth is minimal on women. In Table 6.3, the contribution of income growth in excess of 100% and that of LFP growth in the negative range for northeastern and southern men in the 65-69 age group reflects negative time trends in those two groups. These tables also show the enormous contribution of the pure effect of time, an indistinguishable combination of technological and institutional changes extrapolated from the past but dampened to reflect previous slowing trends. The interpretation of these magnitudes and signs is that, if it were not for the effect of the regional time trend, in both cases the driver percentage in 2025 would be substantially higher than it is with the time trend. GM Project G.6 6 - 11 October 2000
Table 6.2. Determinants of Projected National Driver Growth Men, National Women, National Age Income Employment Time Income Employment Time status trend status trend 65-69 51.21% 2.39% 43.11% 18.08% 0.36% 82.03% 70-74 32.98% 6.90% 59.82% 20.42% 0.80% 79.35% 75-79 28.46% 0.28% 70.79% 9.76% 0.16% 90.34% 80-84 11.06% 0.35% 88.42% 6.26% 0.18% 93.53% 85+ 4.83% 0.06% 95.12% 8.35% 0.04% 91.71% Table 6.3. Determinants of Projected Regional Driver Growth Midwest Men Midwest Women Age Income Employment Time Income Employment Time status trend status trend 65 81.12% 3.16% 15.72% 11.71% 0.22% 88.07% 70 28.12% 7.01% 64.88% 14.65% 0.71% 84.64% 75 13.24% 0.23% 86.52% 4.96% 0.13% 94.91% 80 5.73% 0.26% 94.01% 5.30% 0.15% 94.55% 85+ 3.92% 0.04% 96.03% 6.75% 0.03% 93.22% Northeast Men Northeast Women Age Income Employment Time Income Employment Time status trend status trend 65 106.71% 4.67% -211.38% 41.43% 0.95% 57.62% 70 26.06% 7.14% 66.81% 36.69% 1.50% 61.81% 75 18.59% 0.26% 81.15% 19.65% 0.32% 80.03% 80 7.02% 0.34% 92.64% 5.98% 0.25% 93.77% 85+ 3.80% 0.05% 96.15% 12.23% 0.06% 87.71% Southern Men Southern Women Age Income Employment Time Income Employment Time status trend status trend 65 128.96% 6.06% -35.02% 13.24% 0.26% 86.50% 70 46.43% 7.83% 45.74% 20.71% 0.66% 78.63% 75 43.52% 0.34% 56.14% 9.44% 0.12% 90.44% 80 20.30% 0.48% 79.22% 7.77% 0.17% 92.05% 85+ 7.79% 0.08% 92.13% 9.71% 0.03% 90.26% GM Project G.6 6 - 12 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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Page 46 and 47: Number of Licenses (thousands) 100
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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
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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
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Page 92 and 93: Statistics (BLS) projections beyond
Page 96 and 97: Table 6.3 (continued) Western Men W
Page 98 and 99: Daily Person Trips/Person Figure 7.
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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
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Page 136 and 137: Table 9.1. Sensitivity of Total Dri
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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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