Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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9.3.1 The Effect of Time on VMT Projections The substantial contribution of the technological and institutional changes represented in the time effect of the projections of VMT makes it worthwhile to derive a lower bound case in which none of those changes occur, particularly since we cannot attribute the effects of time to specific developments. Table 9.8 reports the 2025 projections of VMT, at the national level, for men and women with and without any time effects. Eliminating the time effects drops the men’s VMT projections by about 13% and those of women by 35%. Figures 9.3 and 9.4 show the upper and lower bounds of VMT, graphically depicting the difference time makes in these projections. This is a useful opportunity to compare the effects of improving elderly health on VMT relative to the effects of the undifferentiated technical and institutional changes represented by time. Table 9.8 also reports the 2025 VMT for the scenario with no time effects but the health status indicator increasing at ½% per year from 1995 through 2025. The base-case projections developed otherwise have kept the value of the health status variable constant at its 1995 level. Because the 65-69 year-old men’s coefficient for health status was zero (statistically), there is no effect on that group. The remaining age groups of men show a declining impact of improving health status, beginning with over a 9% impact on the 2025 VMT projection for 70-74 men and falling to somewhat over 1% for the 85+ group, relative to a no-time effects projection for 2025. For women, the pattern by age is much the opposite, with a 1% increase in 2025 VMT attributable to improvement in health status among 65-69 women, increasing to a 15% difference in the 85+ group. GM Project G.6 9 - 14 October 2000
Table 9.8. VMT Projections for 2025 With and Without Pure Time Effects and with Improving Health Status, National Level Men Age group Observed With time Without time Change Health improvements, no Change 1995 2025 2025 time effect, 2025 65-69 12,419.43 18,787.30 16,279.84 -13.35% 16,279.84 0.00% 70-74 10,291.61 13,706.88 11,836.52 -13.65% 12,945.25 9.37% 75-79 9,422.77 12,709.24 10,981.64 -13.59% 11,404.77 3.85% 80-84 6,269.21 8,622.38 7,455.89 -13.53% 7,677.05 2.97% 85+ 5,165.97 6,558.96 5,662.48 -13.67% 5,735.99 1.30% Women Age group Observed With time Without time Change Health improvements, no Change 1995 2025 2025 time effect, 2025 65-69 5,841.61 11,275.70 7,324.45 -35.04% 7,417.75 1.27% 70-74 5,054.79 9,435.52 6,118.64 -35.15% 6,290.24 2.80% 75-79 4,288.47 7,317.29 4,713.94 -35.58% 5,003.88 6.15% 80-84 3,805.26 6,818.64 4,398.60 -35.49% 4,590.16 4.36% 85+ 2,780.62 4,946.91 3,188.68 -35.54% 3,667.11 15.00% Figure 9.3. Projected VMT, Men (Upper and Lower Bounds) GM Project G.6 9 - 15 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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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 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 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
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
Page 168 and 169: projections of VMT may be high. Our
Page 170 and 171: of entry/exit, safety, security, et
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
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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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