Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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trend of fatal crashes in the future. Our own empirical examination of the fatal driver crash rate per mile (per hundred million miles, more precisely) showed negative relationships between the crash rate and two important variables that are widely expected to increase over time, elderly income and seat belt use. Pegging female VMT to 1995 male VMT dampened the growth of female elderly VMT over the ensuing thirty-five years, which in turn would have dampened the projection of female driver fatalities. Evidently this source of under-projection did not compensate for the two sources of over-projection— the straight-line extrapolation of VMT growth and the constant crash rate per mile. The Wiggers Projection Wiggers (1999, Table 2) separately projected elder driver and passenger fatalities by the same age groups used here, and by gender, for 2020 and 2030. Wiggers’s methodology assumed that VMT and all other driving behavior would remain constant (1999, p. 4), and he projected driver fatalities as a linear extrapolation of elderly fatality rates per 100,000 population, times population growth. To obtain the trend of elderly fatality rates, Wiggers regressed fatality rates calculated from FARS data on time over the period 1975-1997. To avoid the possibility of projecting negative fatalities for some time in the future, he rejected regressions that yielded negative regression coefficients (1999, p. 4, n. 5). Another motivation for this exclusion of negative coefficients was to represent a diminishing effect of safety improvements on fatalities, but a negative regression coefficient on time would not necessarily imply a non-diminishing effect of safety improvements. (The use of time and the square of time as separate regressors could have accounted for diminishing effectiveness of safety improvements while still permitting them to contribute to reductions of fatalities. A negative coefficient on the time variable and a positive coefficient on time squared would be interpreted as a positive effect of safety technology with diminishing effects over time.) GM Project G.6 10 - 5 October 2000
Wiggers’ projections do not show a simple pattern relative to the ORNL projections. We summarize the out-year projections for Wiggers and ORNL in Table 10.4, which compares the ORNL driver fatality projections for 2025 with Wiggers’ two years, as percents of actual historical driver fatalities. As the first row of Table 10.4 shows, ORNL’s projections are considerably higher than Wiggers’ for 2020 for each male age group except 85+ but are slightly lower than Wiggers’ for 70-74 and 75-79 females. Although ORNL’s 2025 projection and Wiggers’ 2030 projection are not directly comparable, the implied growth trends nonetheless can be compared. Wiggers’ 2030 projections for the three younger male age groups are substantially lower than ORNL’s 2025 projections for those groups and considerably higher for the two oldest male age groups. Wiggers’ 2030 projection for 65-69 females is nearly half again the magnitude of ORNL’s 2025 projection for that group, his projections for 70-74 and 75-79 women are considerably higher than ORNL’s, and the Wiggers 2030 projections for the two oldest groups of women are virtually the same as ORNL’s 2025 projections for those age groups. Table 10.4. Wiggers’ and ORNL’s Projections of Elderly Driver Fatalities as a Percent of Historical Fatalities Males Projection 65-69 70-74 75-79 80-84 85+ 2020 Wiggers* 149 % 145 % 151 % 198 % 319 % 2020 ORNL** 230 % 195 % 208 % 225 % 213 % 2025 ORNL** 267 % 225 % 275 % 295 % 240 % 2030 Wiggers* 157 % 179 % 228 % 360 % 514 % Females Projection 65-69 70-74 75-79 80-84 85+ 2020 Wiggers* 241 % 223 % 215 % 220 % 320 % 2020 ORNL** 327 % 202 % 200 % 306 % 408 % 2025 ORNL** 394 % 243 % 277 % 405 % 481 % 2030 Wiggers* 299 % 320 % 361 % 414 % 491 % * Numbers are given as a percentage of 1993-97 average fatalities ** Numbers are given as a percentage of 1995 average fatalities GM Project G.6 10 - 6 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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Table 6.3 (continued) Western Men W
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Daily Person Trips/Person Figure 7.
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Second, VMT as a consumption item i
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century on which we are unable to o
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The health status effects are posit
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significant one in the final set of
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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
Page 138 and 139: material, dampening effect on drive
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 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
Page 194 and 195: Table A.4.2. Projected Elderly Driv
Page 196 and 197: Table A.4.4. Projected Elderly Driv
Page 198 and 199: Table A.5.2. Projected Total Fatali
Page 206 and 207: Table B.1. Projections of Non-Insti
Page 208 and 209: 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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