Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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9.1.1 Computations The projection of fatalities is a function of the projections of non-institutionalized population, the percent of the population that will drive, the average miles driven per year by an older driver, and the fatal crash rate per hundred million miles driven. To calculate the percentage contributions of the four major components to the projections of total fatalities it is necessary to take account of the multiplicative structure of the projections. The general method of making these calculations is to compare the number of fatalities projected under different circumstances. It is possible to hold the projection of any one of these components, or any combination of them, at their 1995 levels, which we call “flat-lining” in the following explanation. Let A be the projected fatalities derived from the full projection (i.e., when no component projection is flat-lined), and let B be the projected fatalities when all four projection components are held at their 1995 values (i.e., when all four are flat-lined). Then A/B is the ratio of the full contribution of all four projection components to the flat-lined figures. Next, define a separate variable for the fatality projection derived under the flat-lining of three of the four contributing projections. Let C be the projection obtained flat-lining all components but the projected elderly population; let D be the corresponding projection derived from flat-lining everything but the projected older drivers, E the projection flat-lining all but VMT, and F the projection from flat-lining all but crash risk. Dividing C, D, E, and F by B gives each component's contribution to the full projection: A/B=(C/B)(D/B)(E/B)(F/B). To simplify the notation, let C/B=G, D/B=H, E/B=I, F/B=J, and A/B=X. Using this new notation, the expression for the projection in terms of the contributions of individual components is X=GHIJ. To express the total projection as a sum of the components, express the equation in logarithmic form: log X= log G + log H + log I + log J. Finally, to express the individual contributions as shares of the full projection, divide the entire equation by log X: 1 = (log G )/(log X) + (log H)/(log X) + (log I)/(log X) + (log J) /(log X), where (log ! )/(log X) is the percent contribution of factor !, which, of course, will be population, VMT, driver, and risk. GM Project G.6 9 - 2 October 2000
9.1.2 Contributions to Driver Fatalities The dampening effect on projected fatalities of falling crash risk over time simply yields a negative contribution to the risk projection, which means that if risk were flat-lined, the projected fatalities would be larger than they are when the decrease in crash risk is factored into the projection. This negative contribution of crash risk also means that, while the sum of the component contributions to driver fatalities will always be 1, the components themselves have the potential of attaining values greater than one, with the sum of the positive terms, or more simply, those components (VMT, driver, and population) whose changes over time increase the number of fatalities will always sum to a value greater than one. As we can observe in Figure 9.1, by far the largest contributor to the growth in male driver fatalities is population. This should come as no great surprise given that the elderly male non-institutionalized population is expected to double over the projected time frame, while VMT and percentages of people who drive are projected to increase at less substantial rates. The driver projections show up as a mere blip for the younger age groups simply because these groups already have driver rates near 100%. As such, no substantial increases in these rates were possible when projecting, meaning that any increase in fatalities over the projected time frame cannot be attributed in great part to an increase in the percent of the male population that drives. This effect lessens as we move to the oldest age groups. VMT is somewhat similar to driver, with the younger groups already approaching a theoretical limit on the feasible amount of annual VMT, translating into a smaller relative contribution to fatalities. The relative component contributions to female driver fatalities vary greatly from those of male fatalities. Non-institutionalized female population is expected to increase dramatically, but it is not expected to double as it is for men. In addition, projections of VMT and percentage of the population that drives are slightly higher for women then for men when comparing the ratio of 2025 projections to 1995 observed values. As one can see in GM Project G.6 9 - 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
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
Page 90 and 91: more likely to be drivers if there
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 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
Page 152 and 153: Step 3a: Modifying Income at differ
Page 154 and 155: Clicking on one of the sheet tabs w
Page 156 and 157: 10. SUMMARY, CONCLUSIONS, AND RECOM
Page 158 and 159: similarly relied on a regression mo
Page 160 and 161: trend of fatal crashes in the futur
Page 162 and 163: The base of exposure to crash risk
Page 164 and 165: population, and a slightly smaller
Page 166 and 167: Second, improvements in health stat
Page 168 and 169: projections of VMT may be high. Our
Page 170 and 171: of entry/exit, safety, security, et
Page 172 and 173: Blincoe, Lawrence. 1994. Estimating
Page 174 and 175: College of Commerce and Business Ad
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
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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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