Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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Figure 9.4. Projected VMT, Women (Upper and Lower Bounds) 9.3.2 Driver Fatalities with Lower Bounded VMT A lower bound on driver fatality projections uses the lower-bounded VMT projections with no time effects. Table 9.9 reports 2025 driver fatality projections with and without time effects in VMT. Compared to the 1995 reported fatalities, the inclusion or exclusion of time effects makes a second-order difference, at least for men. Without time effects, total male driver fatalities increase by 151% by 2025, compared to 190% with those effects. For women the difference is greater: 143% increase without time versus 275% with. Using the lower-bounded VMT has interesting effects on the relative contributions of the major components to driver fatalities, as depicted in Figures 9.5 and 9.6, compared with Figures 9.1 and 9.2. Eliminating the time effect on VMT alone substantially affects the contribution of driver risk, in both men and women. The greatest dampening effect on male fatalities made by risk, with time effects on VMT, are in the two oldest age groups; without time effects, the greatest effect is on the two youngest groups. With women, the pattern is less easily summarized, but by eliminating the time effect in VMT, the effect of risk rises in GM Project G.6 9 - 16 October 2000
the youngest group and drops in the next-youngest group. The effect of risk stays about the same in 75-79 group, shoots even farther up in 80-84, and drops sharply in the 85+ group. Table 9.9. Comparison of Driver Fatality Projections for 2025, with Upper-bounded and Lower-bounded VMT Projections, National Level Age group Men, with upperbounded VMT Men, with lowerbounded VMT Men, 1995 reported Women, with upperbounded VMT Women, with lowerbounded VMT Women, 1995 reported 65-69 2424 2101 805 1446 940 316 70-74 1735 1498 733 1067 692 362 75-79 1996 1725 613 957 616 322 80-84 1545 1336 469 865 558 196 85+ 706 610 270 559 361 106 Total 8406 7270 2890 4894 3167 1302 Figure 9.5. Component Contributions to Driver Fatality Projections Using Lower-bounded VMT, Men GM Project G.6 9 - 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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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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considerably greater proportional c
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Page 98 and 99: Daily Person Trips/Person Figure 7.
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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 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 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
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Page 168 and 169: projections of VMT may be high. Our
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Page 172 and 173: Blincoe, Lawrence. 1994. Estimating
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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.4.4. 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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