Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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major contributing factor as was driver inattention. Older drivers respond more slowly than younger drivers to unexpected stimuli and tend to be much slower to detect a warning sign, make a decision, and complete a vehicle maneuver (U.S. DOT, 1998, pp. 23-24). All of these traffic conditions contribute to crashes involving the older driver. 3.4.5 Projections of Crashes and Casualties Because of the increasing population of elderly persons (i.e., persons 65 and older) in the next quarter century, historical trends of their increasing mobility (which implied greater exposure to traffic crashes), and historically high fatality “rates”(i.e., fatalities per vehicle miles driven), predictions of a crash problem involving the elderly are impossible to ignore. Burkhardt (1998B, pp. 6-7) projects that the number of elderly-driver-involved fatalities will more than triple between 1995 and 2030, from 7,038 to 23,121 (Burkhardt, 1998B, Figure 2). It should be noted that Burkhardt’s purpose in this document is not to document his methodology for arriving at this projection. Rather, the purpose of the report is to influence policy decisions for ensuring mobility and independence of older drivers into the future. This report is discussed more fully in Section 10.1. Wiggers (1999) projects that elderly traffic fatalities will be about 2.5 times greater in 2030 than they were in the mid-1990s. (Wiggers averages the numbers of elderly traffic fatalities between 1993 and 1997 to use as the comparison number.) He estimates an increase in fatalities from 6,326 to 15,889 (Wiggers, 1999, Table 3). Wiggers also notes a dramatic increase in the number of elderly female drivers in the future. The percentage of female drivers killed will increase from 41% (1993-1997 average) to 59% (2030 projection) of all elderly female traffic fatalities (Wiggers, 1999, pp. vii-viii). This report is also discussed more fully in Section 10.1. Both the Burkhardt and Wiggers projections indicate significant increases in elderly crash-involved traffic fatalities in the next three decades. Based on increasing GM Project G.6 3 - 34 October 2000
elderly population numbers and on elderly drivers’ increased VMT (crash exposure), these increases in the numbers of crash-related fatalities of the elderly seem inevitable. In addition, as shown in Table 3.5, between 1986 and 1996, the percentages of traffic fatalities that represent persons age 65 and over increased faster than the proportion of the elderly within the entire population (Hakamies-Blomqvist, 1999). Table 3.5. Comparison of Changes in Percentages of the Elderly in the General Population and in a Count of Total Traffic Fatalities, 1986-1996 Year Population Over Age 65 (%) Traffic Fatalities of Persons Over Age 65 (%) 1986 12.1% 13% 1996 12.8% 16.9% Even with the “increases” noted above, the cause of death for the elderly in 1996 was 19.8 times more likely to be caused by heart disease and 12.4 times more likely to be caused by cancer than by a traffic crash (U.S. Census Bureau, 1999). The primary causes of death at various ages are shown in Figure 3.7. As can be seen in this figure, traffic-related fatalities are a more common cause of death for age groups under 65 than for the elderly. For example, motor vehicle crashes account for 14.7% of all deaths in age group 15-34, but less than 1% of all deaths for age group 65 and above. GM Project G.6 3 - 35 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
Page 12 and 13: NASS National Automotive Sampling S
Page 14 and 15: Over the past century, the numbers
Page 16 and 17: Figure ES-1. Projected Active Drive
Page 18 and 19: Table ES-1. Elderly Driver Fatality
Page 20 and 21: 1. INTRODUCTION America is graying.
Page 22 and 23: of existing data bases, including h
Page 24 and 25: various data sources and their abil
Page 26 and 27: Finally, data sets should contain,
Page 28 and 29: • Examine data sources to determi
Page 30 and 31: health care, resulting in longer li
Page 32 and 33: Age group Table 3.2. Projected Rati
Page 34 and 35: that individuals will not retire at
Page 36 and 37: analysis. Distribution of the elder
Page 38 and 39: More elderly people live in rural a
Page 40 and 41: assessments of health status) does
Page 42 and 43: 3.2.2 Impacts of Increasing Frailty
Page 44 and 45: current vision screening tests at d
Page 46 and 47: Number of Licenses (thousands) 100
Page 48 and 49: 3.3.2 Changes in Driving Habits In
Page 50 and 51: Transportation Survey (NPTS) data,
Page 52 and 53: tourist services, and “smart” s
Page 54 and 55: Specific highway enhancements to re
Page 56 and 57: 3.4.2 Other GM Project Results (Pro
Page 58 and 59: • As age increased, being struck
Page 60 and 61: men using mid-1990s data, this gend
Page 64 and 65: Total (percent) 100 80 60 40 20 0 A
Page 66 and 67: cohort behavior and conditions for
Page 68 and 69: 4.2.3 Drivers and Driving Distance
Page 70 and 71: The HRS and the AHEAD studies are c
Page 72 and 73: these data and weights. Because of
Page 74 and 75: U.S. Census Bureau (http://www.cens
Page 76 and 77: 5. OVERVIEW OF THE MODELING SYSTEM
Page 78 and 79: or acquisition costs, of the goods
Page 80 and 81: institutionalized settings, our pro
Page 82 and 83: elationships between health and the
Page 84 and 85: 6. THE PROPORTION OF THE ELDERLY PO
Page 86 and 87: Employment status represents a deri
Page 88 and 89: statistical relationship exists bet
Page 90 and 91: more likely to be drivers if there
Page 92 and 93: Statistics (BLS) projections beyond
Page 94 and 95: considerably greater proportional c
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
Page 102 and 103: century on which we are unable to o
Page 104 and 105: 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
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Table 7.3. Determinants of Projecte
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8. FATAL CRASH RATES 8.1 MODELING F
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The other fatality risk concept we
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dropped. Since these measures have
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have a regular pattern over the age
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Table 8.2. Total Risk Crash Rate Re
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projection the asymptotic behavior
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GM Project G.6 8 - 13 October 2000
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GM Project G.6 8 - 15 October 2000
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percentages of people driving, and
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9.1.1 Computations The projection o
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Figure 9.2, the combination of thes
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Table 9.1. Sensitivity of Total Dri
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material, dampening effect on drive
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Table 9.2. Sensitivity of 2025 Proj
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Age group Table 9.6. Sensitivity of
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9.3.1 The Effect of Time on VMT Pro
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Figure 9.4. Projected VMT, Women (U
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Figure 9.6. Component Contributions
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Step 1: Introduction Step 2: Decisi
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Step 3a: Modifying Income at differ
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Clicking on one of the sheet tabs w
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10. SUMMARY, CONCLUSIONS, AND RECOM
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similarly relied on a regression mo
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trend of fatal crashes in the futur
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The base of exposure to crash risk
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population, and a slightly smaller
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Second, improvements in health stat
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projections of VMT may be high. Our
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of entry/exit, safety, security, et
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Blincoe, Lawrence. 1994. Estimating
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College of Commerce and Business Ad
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National Association of Development
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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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