Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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elationships between health and the decision to drive and between health and VMT is recommended in Chapter 10. Table 5.1. NHIS Activity Limitation Status Regression Coefficients and T-Values 1983* 1990 1995 R-squared 0.117095 0.102946 0.109982 ß t ß t ß t Intercept 2.0628 24.07 2.0933 26.29 1.9726 23.56 Northeast 0.0493 2.16 0.0401 1.90 0.0946 4.24 Midwest 0.0269 1.21 0.0111 0.54 0.0817 3.69 South 0.0147 0.71 -0.0331 -1.68 -0.0034 -0.16 Gender (male=1) 0.1552 1.72 0.3392 3.73 0.3337 3.40 Ageclass 45-49 0.5070 8.62 0.5929 10.26 0.7606 11.88 50-54 0.4058 6.76 0.5261 8.98 0.6702 10.48 55-59 0.3017 4.93 0.5111 8.67 0.6218 9.80 60-64 0.3031 4.94 0.4102 7.00 0.4601 7.22 65-69 0.3241 5.28 0.3128 5.34 0.4622 7.22 70-74 0.8146 13.22 0.8080 13.51 0.9262 14.21 75-79 0.7290 11.36 0.6748 10.93 0.7318 10.77 80-84 0.3620 5.29 0.4982 7.47 0.5847 8.09 Gender-ageclass M 45-49 -0.2082 -2.18 -0.3631 -3.78 -0.3939 -3.77 M 50-54 -0.2028 -2.09 -0.4254 -4.37 -0.3884 -3.71 M 55-59 -0.1971 -2.00 -0.4962 -5.07 -0.5508 -5.28 M 60-64 -0.3692 -3.71 -0.5885 -6.01 -0.5979 -5.70 M 65-69 -0.5231 -5.24 -0.5892 -5.99 -0.7184 -6.78 M 70-74 -0.2198 -2.17 -0.3658 -3.64 -0.3196 -2.96 M 75-79 -0.2785 -2.64 -0.2835 -2.71 -0.1937 -1.72 M 80-84 0.0264 0.23 -0.2865 -2.54 -0.1765 -1.42 Race 0.0673 2.98 -0.0025 -0.12 0.0921 3.81 Married 0.0843 4.67 0.0470 2.76 0.0151 0.83 years of school** 0.2962 12.89 0.3012 14.67 0.2718 14.18 Income < $10K -0.9489 -29.97 -0.8093 -28.53 -0.7569 -22.92 $10-20K -0.5825 -23.64 -0.4976 -21.93 -0.4001 -13.07 $20-30K -0.3718 -15.81 -0.2668 -11.75 -0.1583 -5.13 $30-40K -0.1542 -6.23 -0.1524 -6.29 -0.0880 -2.68 $40-50K -0.0985 -3.82 -0.0601 -2.32 -0.0518 -1.41 *(Note that the 1983 NPTS does not contain a region variable and thus predictions for that year were obtained omitting the region portion of the model). ** The years of school variable is computed by taking the number of years schooling a given individual had, adding one, and taking the natural log of the result. GM Project G.6 5 - 7 October 2000
5.5 ASSUMPTIONS As noted in Section 4, our search for data sets on the elderly which include information on health, income, education, driving status and practices (e.g., seat belt use, driving while intoxicated, use of transit), and VMT/person, while also including variables for gender, age group, and region, concluded that these data sets simply do not exist. In addition, it is not generally possible to form direct links between data sets. Therefore, for the critical missing link of health status, we constructed the surrogate described in the preceding section. In other cases, we simply excluded a particular projection from our model. One example of such an exclusion is the future impact of technology on the elderly driver. Improved infrastructure, in-vehicle devices, and ITS innovations (see also Sections 3.3.5 and 10.3.1) may have a tremendous impact. We simply do not know how these innovations will impact the older driver (or how they will influence the elderly decision to choose alternative transportation or to walk). We also do not know how public policy will affect the building of new highways or affect our transportation modes. Finally, because of the tremendous influence of this large elderly population in the future, changes in public transportation will almost certainly occur. Predicting the extent of these changes, however, would be mere speculation. Using traditional regression models on historical data is an effective way to estimate the effects of predictive variables on components of our modeling system. However, assuming that the magnitude of historical trends will continue indefinitely into the future sometimes yields absurd results. In situations where this problem presents itself in our projections, we logically modify coefficients of variables to provide more reasonable estimates of our modeling system components. For example, the probability of driving must be between 0 and 1 and projections of VMT by elderly drivers should not exceed a logical maximum, explored in Chapter 7. GM Project G.6 5 - 8 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
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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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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
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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,
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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
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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
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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 96 and 97: Table 6.3 (continued) Western Men W
Page 98 and 99: Daily Person Trips/Person Figure 7.
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Page 108 and 109: empirically derived regression coef
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Page 112 and 113: Table 7.3. Determinants of Projecte
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Page 118 and 119: dropped. Since these measures have
Page 120 and 121: have a regular pattern over the age
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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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