Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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percentages of people driving, and decreases in the fatal crash rate of drivers. For male older drivers aged 65-69, the number of annual driver fatalities is projected to increase 166.6%, from 614 to 1637. For females of the same age group, fatalities are projected to increase by 294%, from 267 to 1051. This greater increase is due in large part to greater projected increases for women in VMT and the percent of women who drive. Note that, although the rate of increase in the number of fatalities is expected to be higher for women during 1995- 2025, the absolute number of female driver fatalities will remain lower than that of male drivers. In the regional breakdown of driver fatalities, the greatest number occurs, as expected, in the South. In 1995, 42% of all driver fatalities, nationally, occurred in the South. In 2025, our projections indicate that this number will rise to 51%, mostly due to higher than average expected population growth of the elderly in this region. The projections of our total fatalities measure mirrors the trends found in driver fatalities, from the higher rate of increase in total fatalities attributed to women, to the increase in the already high proportion of fatalities occurring in the South. We do not present the numbers for those projections in either tables or graphs since they were so similar to the driver fatality results. Additionally the implicit attribution of fault in that measure lends those numbers to easy misinterpretation. GM Project G.6 8 - 17 October 2000
9. ANALYSIS OF THE FATALITY PROJECTIONS In this chapter, we offer two views on the importance of various determinants of elderly driver fatalities. The first takes the perspective of the four major components of the projections: growths in population, increases in VMT, growth in the percent of the elderly population driving, and decreases in crash risk. We report these contributions to the fatality projections in Section 9.1. None of these major components is directly amenable to policy instruments, but many of the variables in the equations used to project those components do have some susceptibility to adjustment by public policy. In Section 9.2 we report the sensitivity of driver fatalities in 2025 to alternative scenarios based on alternative growth assumptions of each of the independent variables in the component equations, one at a time. Thus, we can see how sensitive the driver fatality projections are to slight changes in, say, income or health status. Pursuing the findings of the contributions and sensitivity analyses of this chapter, Section 9.3 reports the consequences of suppressing the effects of unspecified technological and institutional changes on VMT and on driver fatalities. A spreadsheet tool was developed to let users conduct scenario analyses of particular interest to themselves by modifying growth assumptions used in our projections. The guide to use this tool is in Section 9.4. 9.1 CONTRIBUTIONS OF INDIVIDUAL COMPONENTS TO DRIVER FATALITY PROJECTIONS Before reporting the contributions of the major projection components to driver fatalities in 2025, we detail the method of calculating these contributions. Section 9.1.2 offers a graphical presentation of the various components’ percentage contributions to driver fatalities. GM Project G.6 9 - 1 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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Page 86 and 87: Employment status represents a deri
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Page 92 and 93: 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
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Page 108 and 109: empirically derived regression coef
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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 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
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Page 162 and 163: The base of exposure to crash risk
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Page 166 and 167: Second, improvements in health stat
Page 168 and 169: projections of VMT may be high. Our
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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
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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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