Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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C.3. DECREASING GROWTH RATES OF DRIVER PROJECTIONS We constructed these decreasing growth rates by comparing the growth rates (the coefficient of the year trend variable) estimated on 1977-95 and 1977-90 samples; these results are reported in Table C.2. With declining growth rates, we expected the estimated coefficients on the time trend to be larger in absolute magnitude in the shorter sample period (1977-90) than in the longer one (1977-95), and in all but a very few cases this expectation proved correct. We used the ratio of the coefficient values from the 1977-95 sample to those from the 1977-90 sample to represent the pattern of declining growth rate to use in the future. This procedure yielded ratios of later growth rates to earlier growth rates, which were smaller than one. In the few cases in which the longer sample period yielded larger time trend coefficients, we interpolated the ratio of growth rates from adjoining age/gender groups. In projecting the driver rates, we multiplied each subsequent year’s time trend coefficient by this ratio. For example, in projecting the 2000 driver percentages, we multiplied the estimated age-and-gender-specific time trend coefficients by the corresponding growth rate factors to obtain a lower time effect; then in projecting the 2000 driver percentages, we multiplied the growth factor used for the 1995-2000 projection by the same growth-rate factor, further depressing the pure time effect. This method of creating a decreasing autonomous effect of time improved the projections— using the estimated time coefficients generally yielded values of one for every age and gender group by 2010 to 2015— but still yielded age and gender patterns of driver rates that were not “smooth” in the sense of having older groups systematically having lower driver ratios at each projection time. The set of modified time-trend coefficients performed satisfactorily in projections for 65-69 males, which we considered the base group against which the behavior of the other groups reasonably could be compared. We had clear anticipations (Bayesian priors) on how the other older age groups should behave over time (and by 2025) relative to this group: lower driver ratios for each older age group, and generally lower rates for women, which would otherwise parallel the trend for men of corresponding age unless influenced otherwise by other variables. While the ratio-adjusted GM Project G.6 C - 2 October 2000
time coefficients did not yield such a pattern outright, across all age groups and between genders, it did give some key points, however— the 75-79 men, the 85+ age group for males in the Northeast and South, and the 85+ women in the Midwest and Northeast. Consequently, using the 65-69 men, the 75-79 men, and the two groups of 85+ men and women, we further adjusted the already-adjusted time coefficients on the remaining age/gender/region groups to make them fit the pattern of fall-offs with age that our priors gave us. To get acceptable time coefficients for the 65-69 women with which to begin the female pattern of age-specific driver ratios, we compared their 1995 actual driver ratios with the 65-69 men's 1995 actual ratios and found a set of time coefficient values that aligned the 2000 projections between the 1995 actual values and the 2025 ratios projected using the empirically adjusted time coefficient ratios for the 85+ women in the Midwest and Northeast. GM Project G.6 C - 3 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
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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,
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tourist services, and “smart” s
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Specific highway enhancements to re
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3.4.2 Other GM Project Results (Pro
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• As age increased, being struck
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men using mid-1990s data, this gend
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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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Page 172 and 173: Blincoe, Lawrence. 1994. Estimating
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Page 176 and 177: National Association of Development
Page 178 and 179: Suen, S. Ling, C. G. B. Mitchell, a
Page 180 and 181: GLOSSARY Baby boomers Persons born
Page 182 and 183: Table A.1.2. Projections of Vehicle
Page 184 and 185: Table A.1.4. Projections of Vehicle
Page 186 and 187: Table A.2.2. Projections of Drivers
Page 188 and 189: Table A.2.4. Projections of Drivers
Page 190 and 191: Table A.3.2. Elder Driver Fatality
Page 192 and 193: Table A.3.4. Elder Driver Fatality
Page 194 and 195: Table A.4.2. Projected Elderly Driv
Page 196 and 197: Table A.4.4. Projected Elderly Driv
Page 198 and 199: Table A.5.2. Projected Total Fatali
Page 206 and 207: Table B.1. Projections of Non-Insti
Page 208 and 209: Table B.3. Projections of Non-Insti
Page 210 and 211: B.2. INDEPENDENT VARIABLES PROJECTI
Page 212 and 213: Table B.5. Male DRI Income Projecti
Page 214 and 215: B.2.1.2. Projection Adjustments to
Page 216 and 217: B.2.5 Seatbelt Use Our projections
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Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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