Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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5. OVERVIEW OF THE MODELING SYSTEM 5.1 INTRODUCTION TO MODELING Our approach to projecting the number of fatalities involving older drivers used four distinct components: 1. projection of non-institutionalized population, 2. projection of the percentage of that population that drives, 3. the projected average miles driven (VMT) per driver, and 4. the projection of fatal crash risk associated with each mile driven. The non-institutionalized portion of the elderly population that actually drives represents the population exposed to driving risks. We chose not to use the number of drivers’ licenses issued to represent the number of older drivers because many older drivers retain their licenses only for identification purposes. Our methodology for projecting the percentage of elderly drivers is given in Chapter 6. VMT was used to represent the exposure to crash risk as a likelihood of being involved in highway crashes per mile of vehicular travel. Our methodology for projecting VMT is given in Chapter 7, while our methodology for projecting the fatal crash risk is given in Chapter 8. The population projections provided by the U.S. Census Bureau indicate 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. Beginning with non-institutionalized population as a foundation, we used the four components to derive the fatality projections. First, we took the non-institutionalized population projections, sorted by age groups, gender, and region, and multiplied those GM Project G.6 5 - 1 October 2000
estimates by the projected percentages of the elderly population that will be driving, which gives us a total number of older drivers in the future. Then, we took the projected number of older drivers and multiplied it by the average VMT per driver to obtain a total number of miles driven by elderly drivers. Finally, we multiplied the estimated total number of miles driven by the crash risk per mile driven to find our ultimate goal, the projected total number of fatalities (either driver fatalities or total fatalities, as explained in Chapter 8). The behavior of the individual elderly person was taken into account in projecting two of the final three components of the system – when we look at how much an individual with certain characteristics will drive or whether he or she will be a driver. The behavior underlying each of these components is modeled empirically with regression analysis of data from the NPTS, the NHIS, and several other related sources. The estimated equations serve as predictive models used to project our components into the future as the independent variables of those regressions change predictably. NPTS survey data on individual drivers are available to estimate VMT and the probability that an older person will continue to drive. Our projections, however, are in the form of averages for particular subgroups of drivers (e.g., southern male drivers between 65 and 69 years old). The effects of our independent variables in these two models are also estimated at the individual level, while projections are in the form of averages for subgroups. We make the step from characteristics of the individual to characteristics of the group assuming that such groups will be homogeneous over time, and representative of the “average”driver. 5.2 BRIEF EXPLANATION OF DEMAND THEORY Demand theory underlies the projection of the demand for VMT and the need a person has to be a driver. This theory predicts that the quantities of goods or services chosen by a consumer will respond to changes in the consumer’s income and to changes in the prices, GM Project G.6 5 - 2 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
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
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 78 and 79: or acquisition costs, of the goods
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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
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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
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Page 112 and 113: Table 7.3. Determinants of Projecte
Page 114 and 115: 8. FATAL CRASH RATES 8.1 MODELING F
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
Page 122 and 123: Table 8.2. Total Risk Crash Rate Re
Page 124 and 125: 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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