Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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Transportation Survey (NPTS) data, Burkhardt et al. (1998) predicted that annual miles driven by the elderly would increase dramatically by 2030. In a separate article, Burkhardt (1998A) estimated that between 1990 and 2020, the total annual mileage driven by male older drivers will increase by 465% – and, Burkhardt asserted, this estimate is conservative. For senior women drivers, the total annual vehicle miles driven will increase almost 500%. Although the total annual VMT per person of elderly drivers is predicted to increase (Section 7), it is expected that the elderly will almost certainly continue to drive fewer miles than drivers in the labor force. Persons who drive less generally do most of their driving on local roads, rather than expressways. The crash rate on local streets, which include problems such as congestion, difficult intersections, pedestrians, and other hazards, is higher than that of divided highways with controlled access (See also Section 3.4.1.). Because it is impossible to quantify what percentage of the VMT of older drivers is actually driven on the different types of roads, however, this phenomenon was not factored into the ORNL model. 3.3.4 Alternative Transportation Modes Scott Bogren, editor of Community Transportation, has stated that “Today, senior transportation services are one of the fastest growing segments of the transit industry.” (Bogren, 1998, p. 5). To determine the most appropriate alternative transportation option for the elderly, several criteria must be addressed. First, the physical, functional, and budgetary constraints of the elderly person desiring alternative transportation must be considered. Second is the geographic location. Because most transportation providers need to make a profit, public transportation is more prevalent in urban or more densely populated areas. For areas with limited appeal for “typical” public transportation, alternative service options (e.g., public agencies, volunteer groups, etc.) and other innovative options may be available (Suen, 1999). GM Project G.6 3 - 22 October 2000
The typical city bus is not the transit service desired by seniors. Many seniors are concerned that they will fall while boarding or departing from the bus or during transit if they must stand while the vehicle is moving. In addition, because they don’t want to be considered old and slow, they feel rushed to pay their fares and find a seat. “Smart” fare cards, better on-board displays, auditory cues, and other devices will assist the elderly passenger and perhaps encourage more transit use (Suen, Mitchel, and Henderson, 1998). An example of an alternative senior transportation service is the Independent Transportation Network (ITN) operating within a 15-mile radius of Portland, Maine. The ITN, which uses automobiles to transport elderly riders at the riders’ convenience, is one innovation which seems to have gained approval among its elderly riders. Additional mobility options in some locations include pedestrian facilities. For seniors, walking is the second most important travel mode after the private vehicle. Pedestrian accommodations to aid elderly pedestrians at intersections include crosswalk lighting and better signs. For any alternative transportation option, the elderly person must feel safe, secure, and free from harassment while occupying the transit vehicle, while waiting for the transportation vehicle to arrive, and while getting to and from the boarding location (U.S. Department of Transportation, 1997A, p. 50). 3.3.5 “Intelligent” Technologies and Other Adaptive Equipment The Intelligent Transportation System (ITS) is developing highway and in-vehicle devices that would help seniors maneuver better. Current ITS applications include route guidance mechanisms, emergency vehicle location and response systems, adaptive cruise control, vision enhancement, and collision warning systems. Because of their fairly recent incorporation within the market and their relative expense, these systems are not currently widespread among the older driver population (Caird, 1999, p. 3). Hazard warnings, GM Project G.6 3 - 23 October 2000
Page 1: 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 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 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.
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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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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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Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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