Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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B.2.5 Seatbelt Use Our projections on seatbelt use assumed that the goal of 85% usage will be met in the year 2025. Values in between 1995 and 2025 were computed using a simple linear regression involving time for NHTSA seatbelt use data between 1991 and 1995 and the 2025 expected rate (see Table B.10). This simple linear regression is described in the equation below: Projected Seatbelt Usage = 0.5731 + 0.0066*year (where year = 2000, 2005, etc.) This regression equation had an R 2 value of 0.9174. Table B.10. National Seat Belt Use Projections 1995 2000 2005 2010 2015 2020 2025 68.0% 69.2% 72.5% 75.8% 79.1% 82.4% 85.0% B.2.6 Urban We maintained 1990 percentages of urban population provided by the Census Bureau in our projections. For these 1990 levels, see Table B.11. Table B.11. Percent of Population in Urban Areas, 1990 Census Nation Nation 1990 76.1% Midwest 71.7% Northeast 78.9% South 68.6% West 86.3% GM Project G.6 B - 12 October 2000
APPENDIX C. NOTES C.1. REGIONAL VMT AND HEALTH INDEX COMPUTATIONS FOR 1983 DATA The 1983 NPTS did not contain a variable identifying the respondent’s region of residence. Therefore, in order to obtain the average health score and vehicle miles of travel numbers needed for the aggregate crash rate model, we took the percent difference between the regional and national figures for the two variables in 1990 and adjusted the national average for each respective gender/age average in 1983 by these regional variation measures. C.2. STARTING VALUES FOR VMT VALUES OF SOUTHERN MEN The 1995 starting values used in the projections were slightly modified due to the inconsistencies in historical VMT for Southern men. These inconsistencies involved two cases where an older group had a higher average VMT in 1995 than a younger group. The two cases involved 70-74 Southern men having less VMT than their 75-79 year old counterparts, and the 80-85 group having a lower average than the 85+ group. In order to account for this, a simple iterative proportional fitting procedure was used. Given the simple nature of the problem, this iterative proportional fitting amounted to taking the average of the two groups affected in each case and using that average as the starting point for both groups. In Table C.1 below are the original values observed from the 1995 NPTS for each group, along with their modified numbers used in projections. Table C.1. Average VMT per Person of Southern Men, 1995 NPTS and Adjusted Numbers Age 1995 NPTS Base Used in Projections 70-74 10431 10909 75-79 11388 10909 80-84 4681 5336 85+ 5991 5336 GM Project G.6 C - 1 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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Page 168 and 169: projections of VMT may be high. Our
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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
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 218 and 219: C.3. DECREASING GROWTH RATES OF DRI
Page 220: 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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