Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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10. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS 10.1 SUMMARY OF FINDINGS As part of this research effort, we developed a new methodology for projecting elderly traffic crash fatalities. This methodology separates exposure to crashes from crash risk per se, and further divides exposure into two components, the number of miles driven and the likelihood of being a driver. This component structure permits conceptually different determinants of traffic fatalities to be projected separately and has thorough motivation in behavioral theory. It also permits finer targeting of particular aspects of projections that need improvement and closer linking of projections to possible policy instruments for influencing them. 10.1.1 Aggregate Fatality Projections Tables 10.1 and 10.2 summarize ORNL’s projections of the two fatality measures used in this study, older driver fatalities and the pro-rated total fatalities. Table 10.1 includes fatalities of older drivers only. It is important to recall that our measurement of “total fatalities” (Table 10.2), which extends to passengers in an elder driver’s vehicle and to drivers and passengers of other vehicles as well as non-occupants in such a crash, divides the total number of fatalities among the number of vehicles involved in the crash. This procedure effectively excludes some deaths from these crashes from the final measure of total fatalities assigned to elder drivers. The concept was adopted as a concession to the absence of information on which driver in a crash was at fault. Others’ definitions of “total” fatalities sometimes include the deaths that we have excluded by our pro-rating procedure. GM Project G.6 10 - 1 October 2000
Table 10.1. Older Driver Fatality Projections by Age Group, Male and Female Age Group 1995 2000 2005 2010 2015 2020 2025 65-69 881 983 1115 1425 1911 2287 2688 70-74 956 1045 1040 1158 1447 1888 2216 75-79 876 1136 1212 1235 1398 1793 2419 80-84 704 1064 1325 1448 1511 1770 2329 85+ 474 670 838 1039 1202 1294 1489 Total 3891 4898 5530 6305 7469 9032 11141 Table 10.2. Total Fatality Projections by Age Group, Male and Female 1995 2000 2005 2010 2015 2020 2025 65-69 1121 1267 1464 1917 2628 3221 3871 70-74 1094 1221 1219 1369 1744 2333 2802 75-79 935 1240 1342 1400 1627 2139 2953 80-84 665 1022 1282 1429 1518 1803 2410 85+ 377 530 666 840 990 1082 1265 Total 4192 5279 5973 6955 8508 10579 13301 10.1.2 Comparison of ORNL Aggregate Fatality Projections to Other Studies Comparison of our projections with two other recent sets of projections for elderly traffic fatalities in the United States (Burkhardt et al. 1998; Wiggers 1999) is instructive. The three studies differ in their methodologies and consequently in their projections. To facilitate the comparison we begin with a recapitulation of the ORNL projection methodology. The ORNL Projection The structure of the ORNL projections is a base of exposure to crash risk, multiplied by a measure of the risk per unit of exposure, multiplied in turn by the population expected to drive. VMT by elderly drivers by gender and age group is the exposure base chosen as representing the most proximate measure of the exposure to the risk of a fatal vehicle crash. The projection of VMT was based on a behavioral regression of the demand for VMT in the previous quarter century (1977-1990/1995), with adjustments made to time trend coefficients used in the projections. The projection of the fatal crash risk per hundred million miles driven GM Project G.6 10 - 2 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
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
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 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
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
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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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