Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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Specific highway enhancements to reduce the crash involvement of the elderly have been recommended (Staplin, 1999). These highway design changes include larger and brighter signage and pavement/curb guides (to accommodate problems with nighttime driving); intersection designs and redesigns to improve visibility and ensure standard, consistent positioning of turn/merge lanes and signage (to alleviate the maneuverability problems of the elderly at intersections); improvements in freeway entrance/exit lanes and signage; and improved pedestrian controls and longer crossing times at crosswalks (Staplin, 1999). ITS technologies and adaptive technologies will certainly impact driving strategies of the elderly and may have a significant impact on their crash involvement in the future. The actual extent of these impacts are impossible to quantify at this time, however, and were not factored into our model. 3.4 CRASH LITERATURE To predict the number of older drivers involved in crashes in the future (e.g., 2020), it is necessary to look at crash data from the past. A discussion of available crash data sets is provided in Chapter 4. Sections 3.4.1-3.4.5, below, provide a brief overview of the current literature providing analyses of crash data. With increasing age comes increasing frailty. The increasing risk associated with traffic crashes as drivers age was discussed in Section 3.2.2 and will not be addressed further in this chapter. GM Project G.6 3 - 26 October 2000
3.4.1 Crash Involvement “Crash involvement” may be measured in several different ways. One way is simply to count the number of crashes (or the number of fatal crashes). However, simply counting crashes does not provide an idea of a crash rate. As noted in Hu, Young, and Lu (1993, p. 56), a crash rate can be calculated in several ways, including the following: • Crashes per capita, • Crashes per licensed driver, • Crashes per vehicle miles driven, and • Crashes based on the proportion of drivers at fault. Each of these calculations results in a different viewpoint of the older driver’s crash involvement. For example, based on numbers of licensed drivers, older drivers have fewer crashes than do younger drivers; however, based on miles driven, drivers over age 75 have the highest crash rate of any age group – even teenagers (Eby et al., 1998, p. 1). These crash rates are examined in greater detail in Section 3.4.2 below, which is a synthesis of research conducted as part of the GM settlement agreement. A crash rate based on crashes per mile driven has been used frequently as a measure of risk. According to Janke (1991), however, the use of crashes/VMT exaggerates the accident risk of low-mileage groups – such as the elderly. That is, crashes are not a result of some factor times VMT; crashes occur from exposure to crash risk. VMT is only one component of this risk. Another component is type of roadway. Using1984 data compiled by the California Business, Transportation, and Housing Agency, Janke (1991, p. 184) noted that were 2.75 times more crashes/mile driven occurred on non-freeways than on freeways. Because high-mileage drivers generally amass most of their VMT on freeways, they face less risk of being involved in a crash than low- mileage drivers who drive primarily on local streets and roads (e.g., elderly drivers). Therefore, the high crash rate of elderly drivers (as measured by crashes/VMT) is not necessarily an indicator of their being dangerous drivers. GM Project G.6 3 - 27 October 2000
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ORNL-6963 Projecting Fatalities in
Page 4 and 5: 4. ANALYSIS OF DATA SETS ..........
Page 6 and 7: GM Project G.6 vi October 2000
Page 8 and 9: LIST OF TABLES Table ES-1 Elderly D
Page 10 and 11: 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 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 62 and 63: major contributing factor as was dr
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
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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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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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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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