Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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Table 7.3. Determinants of Projected Regional VMT Growth Men Women Midwest Age Income Employment Time Income Employment Time 65-69 27.05% 38.18% 34.77% 13.40% 18.68% 67.91% 70-74 20.01% 21.30% 58.68% 8.02% 18.19% 73.79% 75-79 26.22% 10.79% 62.99% 6.24% 7.79% 85.97% 80-84 36.30% 10.62% 53.07% 11.98% 7.54% 80.48% 85+ 44.07% 0.00% 55.93% 11.55% 7.58% 80.88% Northeast Age Income Employment Time 65-69 24.11% 39.72% 36.17% Income Employment Time 70-74 19.92% 21.34% 58.74% 9.61% 17.82% 72.57% 75-79 34.31% 9.63% 56.06% 10.74% 7.43% 81.83% 80-84 34.33% 10.94% 54.73% 8.93% 7.80% 83.26% 85+ 41.12% 0.00% 58.88% 10.95% 7.63% 81.43% South Age Income Employment Time Income Employment Time 65-69 22.14% 40.76% 37.10% 13.06% 18.76% 68.18% 70-74 30.30% 18.60% 51.10% 12.30% 17.23% 70.47% 75-79 46.28% 7.89% 45.83% 12.81% 7.25% 79.94% 80-84 55.26% 7.50% 37.24% 14.75% 7.30% 77.95% 85+ 47.96% 0.00% 52.04% 15.29% 7.26% 77.45% West Age Income Employment Time Income Employment Time 65-69 24.75% 39.39% 35.86% 16.92% 17.94% 65.14% 70-74 21.61% 20.88% 57.51% 7.73% 18.23% 74.03% 75-79 41.11% 8.63% 50.26% 9.41% 7.54% 83.06% 80-84 39.83% 10.04% 50.13% 13.27% 7.43% 79.29% 85+ 30.45% 0.00% 69.55% 3.47% 8.26% 88.27% The time trend accounts for the greatest proportion of VMT growth by far among women in all age groups and regions. In the 70-74 and 75-79 age groups, LFP growth contributes more to VMT growth than does income, but those relative proportions reverse in the two oldest age groups. In the 85+ age groups, other-driver growth continues to contribute to VMT growth between half and three-quarters the mileage income growth contributes with the exception of the West, where the proportions are reversed between income and LFP. GM Project G.6 7 - 16 October 2000
The magnitude of what we call “the pure effect of time” for want of a more precise term is striking. This effect is a combination of technological and institutional changes, and its magnitude reflects the relative contribution of this set of factors to VMT growth in the recent past. Even though we have dampened the strength of this effect from what it would have been with a linear extrapolation, its contribution is still large. One implication of this result is that if one finds our VMT projections unbelievably high, it is possible to lower them without altering the effects of either income growth or changes in elderly labor force participation, although such changes would imply particular beliefs about the progress and consequences of future technological and institutional change. GM Project G.6 7 - 17 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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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 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
Page 110 and 111: GM Project G.6 7 - 14 October 2000
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
Page 154 and 155: Clicking on one of the sheet tabs w
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
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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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