Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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population, and a slightly smaller reduction in women’s crash risk than men’s, which outweighed the effects of substantially larger effect of population growth among males. Table 10.5. Elder Driver Fatality Projections for 2025 (as Percentages of 1995 Driver Fatalities) Age Group Male Female 65-69 301.3% 457.8% 70-74 236.7% 295.0% 75-79 325.5% 297.0% 80-84 329.3% 441.5% 85+ 261.1% 526.1% Total 290.8% 375.9% The forces behind the growth of driver fatalities among men and women also differ. In general, population growth had the greatest effect on growth in male driver fatality projections, and growth of driver populations and VMT were the largest influences on the growth of female driver fatalities. Reductions in crash risk were more influential in retarding the growth of female driver fatalities in the 70-74 and 75-79 age groups, and the greatest impact on males ages 80-84. Growth of driver population contributed very little to the growth in male driver fatalities, although it was larger in the 80-84 and 85+ age groups than in the younger groups. Correspondingly, growth of driver population contributed more to growth of female driver fatalities in the two oldest age groups than in the three younger groups. Reductions in crash risk were more important in dampening female fatality growth among the 70-74 and 75-79 age groups than among any other female age group and more than among the corresponding male age groups. GM Project G.6 10 - 9 October 2000
10.2 CONCLUSIONS The increases in the numbers of elderly fatal traffic crashes will likely be large, but much of the growth will simply be a consequence of population growth. Much of the remaining increase will be a consequence of social changes, particularly the growing similarity in male and female social roles. The outlook would be worse, however, were it not for projections of substantial decreases in crash risks. Laying the responsibility for this growth in traffic fatalities at the door of population growth and greater mobility does little to offer constructive public policy to address the problem. However, examination of the forces contributing to the growth of VMT per driver, driver percentages, and crash risks offers some insights into constructing policies. First, through its influence on VMT and driver populations, employment status has a relatively important influence on driver fatality projections. A one percent larger fraction of 65-69 year- old men in the labor force would entail a ½% increase in the number of driver fatalities in that group in 2025. This is a comparison of alternative labor force participation rates in 2025 for this age group of men, not a comparison of their labor force participation rates in 1995 and 2025. For 65-69 year-old women, the same change in labor force participation would be associated with a b% increase in driver fatalities. We consider this a serious sensitivity to the labor force participation rate. Social Security retirement ages have risen by as much as two years for people born in 1960 or later, which definitely will increase the labor force participation rate in this youngest five-year age group of elderly. Many of these people will want to continue working, and keeping them from the labor force would be counterproductive not only from the perspective of the financial viability of the Social Security program but in terms of their own satisfaction with life. Nonetheless, alternatives such as remote working, working from home, and related developments made viable by the current and future revolutions in communications technologies could be exploited to target this group for effective mobility with lower exposure to traffic crashes. GM Project G.6 10 - 10 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
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GM Project G.6 7 - 14 October 2000
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Table 7.3. Determinants of Projecte
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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
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Page 124 and 125: projection the asymptotic behavior
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Page 128 and 129: GM Project G.6 8 - 15 October 2000
Page 130 and 131: percentages of people driving, and
Page 132 and 133: 9.1.1 Computations The projection o
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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
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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
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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 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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Page 210 and 211: B.2. INDEPENDENT VARIABLES 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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