Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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century on which we are unable to obtain direct, quantitative information. Some of these changes include changing family roles that have caused more women to drive; changing urban-suburban relationships that have affected shopping behavior, recreational travel, and the journey to work; and changing labor market conditions that have interacted with both of the previous changes. In the VMT models, a separate regression was estimated for each age group, as shown in Table 7.1. The percents of variance explained by the regressions, also know as the R 2 statistic, are in a satisfactory range for regressions on large sample-size survey data, centering around a range from 0.17 to 0.29. Let us use the men aged 65 to 69 as an example of how to interpret the coefficient results in Table 7.1. The log(income) coefficient of 0.3050 means that, for each 1% increase in income, VMT will increase by 0.3050%. Similarly for the health status variable, a 1% increase in the health status measure will cause a 0.0868% increase in VMT. Having other drivers in the household will lead to a decrease of 0.1279 in log(VMT), not VMT in its standard scale since the other drivers effect was not estimated in log-form. Being part of the workforce will lead to an increase in log(VMT) of 0.4991. Finally, for each additional year, the increase in log(VMT) not attributable to previously accounted for effects like income and health status is 0.0218. Turning to overall trends in the results, the income elasticities of VMT are in the range of 0.20 to 0.46, meaning that each 1% increase in income will lead to an increase in VMT of 0.20% to 0.46%. This range of increase was not consistently higher across age groups for men or women. The elasticity estimate for 85+ men is 0.80 and -0.69 for 85+ women, with both being statistically significant at 5% or better. The male estimate is somewhat high to be entirely credible and the large negative estimate for the women surely is a fluke of some sort, either statistical or involving peculiarities of income in this age group of women. Section 7.3 contains an explanation of how we handle such odd estimates. GM Project G.6 7 - 6 October 2000
GM Project G.6 7 - 7 October 2000 Table 7.1. VMT Regression Results 65-69 70-74 75-79 80-84 85+ ß T Adj ß ß T Adj ß ß T Adj ß ß T Adj ß ß T Adj ß (if appl) (if appl) (if appl) (if appl) (if appl) Men log (income) 0.3050 3.75 0.2542 4.32 0.3419 4.26 0.4674 3.34 0.8097 1.98 0.3818 log (health score) 0.0868 0.86 0 1.2270 3.64 0.5180 2.00 0.3366 1.35 0.4023 0.1753 0.36 "other driver" -0.1279 -1.79 0 -0.1061 -1.25 -0.3511 -3.33 -0.3254 -2.07 -0.8085 -2.26 -0.1800 employment status 0.4991 6.85 0.2945 2.76 0.5634 3.50 0.3870 1.14 0.5672 0.50 0 year Women 0.0218 3.51 0.0050 0.0394 5.16 0.0050 0.0343 3.35 0.0050 0.0314 2.24 0.0050 0.0543 1.72 0.0050 log (income) 0.2096 2.86 0.2400 0.3834 6.22 0.2000 0.1418 1.38 0.2322 0.0559 0.34 0.2000 -0.6981 -1.73 0.2000 log (health score) 0.0743 0.62 0.1751 0.3215 0.80 0.3816 0.5357 1.55 0.8129 0.5816 1.99 1.9193 2.28 "other driver" -0.4477 -5.60 -0.6983 -7.50 -0.4888 -0.7541 -5.58 -0.6250 -0.5765 -2.55 -0.1168 -0.25 0 employment status 0.7168 6.98 0.5933 4.39 0.4153 0.4357 1.49 0.8867 2.03 0.4500 0.7260 0.94 0.4500 year 0.0203 2.82 0.0150 0.0042 0.48 0.0150 0.0301 2.48 0.0150 0.0207 1.09 0.0150 0.0282 0.57 0.0150 R-Squared .2122 .1747 .2339 .1890 .2962 # of observations 2492 1746 968 430 107
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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
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Finally, data sets should contain,
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• Examine data sources to determi
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health care, resulting in longer li
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Age group Table 3.2. Projected Rati
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that individuals will not retire at
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analysis. Distribution of the elder
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More elderly people live in rural a
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assessments of health status) does
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3.2.2 Impacts of Increasing Frailty
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current vision screening tests at d
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Number of Licenses (thousands) 100
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3.3.2 Changes in Driving Habits In
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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 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
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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 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
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
Page 130 and 131: percentages of people driving, and
Page 132 and 133: 9.1.1 Computations The projection o
Page 134 and 135: Figure 9.2, the combination of thes
Page 136 and 137: Table 9.1. Sensitivity of Total Dri
Page 138 and 139: material, dampening effect on drive
Page 140 and 141: Table 9.2. Sensitivity of 2025 Proj
Page 142 and 143: Age group Table 9.6. Sensitivity of
Page 144 and 145: 9.3.1 The Effect of Time on VMT Pro
Page 146 and 147: Figure 9.4. Projected VMT, Women (U
Page 148 and 149: Figure 9.6. Component Contributions
Page 150 and 151: 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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