Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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Employment status represents a derived demand for the decision to drive. The year variable captures a composite measure of the societal changes we are unable to quantify individually. Health status captures a person’s ability to drive, as a measure with some degree of continuity, rather than his or her preference for driving. Because the fraction of the population that can be drivers is bounded above by the value 1.0, we estimated the percentage of the elderly population that continues to drive with a logistic regression with the functional form of: Prob (continuing to drive) = (1+e -Z ) -1 , where Z = constant + a 1 log (income) + a 2 log (health status) + a 3 (urban) + a 4 (employment status) + a 5 (other driver available in household) + a 6 (year). This equation was estimated separately for age groups and by gender, with ten regressions in all. Table 6.1 contains the results of the regressions of the model. The percent of variance in the dependent variable explained by the regressions ranges from .19 to .50, with these measures showing stronger relationships between the decision to drive and our predictors in women than in men. These adjusted R 2 's, the statistics that report the percent of variance explained in each regression, are satisfactory considering the large sample sizes and the survey character of the data. Due to the form of equation (1), our regressions, and logistic regressions in general, do not have explicitly interpretable coefficients. Note the coefficients in Table 6.1 for the men aged 65-69 as an example. The log(income) coefficient of 0.5829 means that for each GM Project G.6 6 - 3 October 2000 (1)
Table 6.1. Regression Results for the Probability of Continuing to Drive 65-69 70-74 75-79 80-84 85+ ß Prob ? 2 ß Prob ? 2 ß Prob ? 2 ß Prob ? 2 ß Prob ? 2 Men intercept -3.7383 0.0001 -5.3709 0.0001 -3.6958 0.0001 -2.8259 0.0001 -2.3927 0.0052 log (income) 0.5829 0.0001 0.4928 0.0001 0.4150 0.0001 0.2469 0.0003 0.1838 0.0681 log (health) 0.4454 0.0001 1.5087 0.0001 0.7475 0.0001 0.6440 0.0001 -0.0069 0.9633 urban -0.8591 0.0001 -0.4503 0.0001 -0.2979 0.0001 -0.2447 0.0010 -0.2999 0.0079 "other driver" 0.3796 0.0001 0.1985 0.0005 -0.2017 0.0017 -0.0645 0.4189 -0.5744 0.0001 employ status 0.0742 0.2366 0.6539 0.0001 0.3555 0.0019 0.6587 0.0026 0.1682 0.5429 year (MW) 0.0025 0.6205 0.0331 0.0001 0.0216 0.0002 0.0509 0.0001 0.1064 0.0001 year (NE) -0.0283 0.0001 0.0326 0.0001 0.0304 0.0001 0.0318 0.0001 0.0642 0.0001 year (S) -0.0015 0.7259 0.0254 0.0001 0.0213 0.0001 0.0485 0.0001 0.0498 0.0001 year (W) 0.0189 0.0012 0.0119 0.0272 0.0295 0.0001 0.0651 0.0001 0.0723 0.0001 Rescaled R-Squared .2926 .3058 .1920 .2938 .3881 # observations 2498 1976 1098 663 236 Women intercept -3.7481 0.0001 -6.0459 0.0001 -4.6065 0.0001 -3.6567 0.0001 -5.5528 0.0001 log (income) 0.3712 0.0001 0.4649 0.0001 0.2911 0.0001 0.2224 0.0002 0.3887 0.0001 log (health) 0.8317 0.0001 1.5064 0.0001 1.2342 0.0001 1.0860 0.0001 -0.0980 0.5695 urban -0.4853 0.0001 -0.3577 0.0001 -0.2258 0.0001 -0.8416 0.0001 -0.4274 0.0001 "other driver" -0.3855 0.0001 -0.5816 0.0001 -0.5810 0.0001 -1.3106 0.0001 -1.2254 0.0001 employ status 0.2307 0.0001 0.7273 0.0001 0.6524 0.0001 0.6028 0.0002 1.7372 0.0001 year (MW) 0.0433 0.0001 0.0338 0.0001 0.0574 0.0001 0.0848 0.0001 0.0809 0.0001 year (NE) 0.0010 0.0052 0.0130 0.0009 0.0192 0.0001 0.0582 0.0001 0.0493 0.0001 year (S) 0.0348 0.0001 0.0341 0.0001 0.0527 0.0001 0.0589 0.0001 0.0810 0.0001 year (W) 0.0669 0.0001 0.0355 0.0001 0.0526 0.0001 0.0870 0.0001 0.0649 0.0001 Rescaled R-Squared .4355 .3471 .4009 .4986 .3295 # observations 2829 2326 1484 913 468 one unit increase in log(income), the logit function, represented by Z in equation (1), increases by 0.5829. While the precise effect of log(income) on the probability of driving is dependent upon magnitudes of the other effects in the model, one can say that there is a positive relationship between income and the probability to continue driving. The 0.0001 in the “Prob ? 2 ” column of Table 7.1, being lower than 0.05 (or 5%), indicates that a strong GM Project G.6 6 - 4 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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Age group Table 3.2. Projected Rati
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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 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 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 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
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
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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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