Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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Over the past century, the numbers of highways, vehicles, and drivers have increased dramatically. It is easy to allow the imagination to run rampant concerning the upcoming quarter century, to visualize super highways and technology-enhanced vehicles and a multitude of elderly, perhaps unsafe, drivers. Because of these concerns, Oak Ridge National Laboratory (ORNL) was tasked with developing a projection system to determine the impact of the elderly driver in the future. ORNL’s system of projection models provides national estimates and estimates for each of the four Census regions, in five-year increments between the year 2000 and the year 2025, for: follows: • The number of older drivers in the future who will still be driving, by age group and gender, • The average number of miles to be driven annually by an elderly driver, by age group and gender, • The total number of elderly driver fatalities resulting from crashes in which older drivers are involved, and • The total number of all occupant and non-occupant fatalities (all ages) resulting from crashes in which older drivers are involved. Our approach to developing a logical, transparent, and defensible model was as • Review the literature to ascertain the current state of the research and to identify issues. • Examine data sources to determine compatibility between modeling issues and data. • Build a mathematical link between two national surveys so that information on health status could be included in the model. • Develop three empirical models (percentage of drivers, miles driven, and crash rate) based on historical data. • Formulate assumptions on the projections of independent variables in the empirical models for the years 2000-2025. GM Project G.6 ES - 2 October 2000
• Adjust some empirically estimated parameter values in the models, primarily time trends. • For the years 2000-2025, project the number of older drivers, the average vehicle miles traveled (VMT) per older driver, and crash rates, in order to generate projections of highway traffic fatalities by age group, gender, and Census region. • Analyze the projections in terms of the extent to which various factors contribute to the increased fatalities. Thus, our approach to projecting the number of fatalities involving older drivers uses four distinct components: (1) projection of the non-institutionalized population, (2) projection of the percentage of that population that drives, (3) projection of the average miles driven per driver, and (4) projection of the fatal crash risk associated with each mile driven. Our methodology, which has its motivation in behavioral theory, separates exposure to crashes from crash risk per se. It further divides exposure into two components, VMT and the likelihood of being a driver. This component structure permits conceptually different determinants of traffic fatalities to be projected separately. It also permits finer targeting of particular aspects of projections that need improvement and closer linking of projections to possible policy instruments for influencing them. The first component of our model, population projections provided by the U.S. Census Bureau, indicates that nearly all subgroups of the elderly population will increase from 50 to 150 percent by the year 2025. This enormous increase will have a substantial effect on the projection of the absolute number of casualties, even when crash risk per mile driven decreases or remains constant. The second component of our projection system involves estimation of the historical determinants of people’s decisions to drive. Figure ES-1 shows the projected levels of drivers between 1995 and 2025 for males and females. GM Project G.6 ES - 3 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 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 62 and 63: 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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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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