Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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cohort behavior and conditions for the years on which the model is based (i.e., 1977, 1983, 1990, 1995). 4.2 ASSESSMENT OF DATA SOURCES We collected and evaluated data from many sources before determining the most useful and reasonable data sets for use in the model. Data sets were examined for their availability, completeness, quality, and appropriateness for the purposes of the project. Each data set is described briefly below under one or more of the categories of information needed for the projection model. Reasons for inclusion or exclusion of data sets are provided. 4.2.1 Population Data and Projections (U.S. Census Bureau) Population data from the U.S. Census Bureau (http://www.census.gov), with historical data dating back to 1790 and projections through 2100, were the easiest to obtain and most complete data sets available. Appropriate as a measure of the rapid increase in the numbers of elderly after 2010, population projections also included considerations of migration, immigration, emigration, birth/death rates, and other population-changing influences. Population data could be obtained for every permutation needed by the model. Additional information on how population data were used in the model is given in Section 5.3 and Appendix B.1. 4.2.2 Income Data (Bureau of Economic Analysis, U.S. Census Bureau, and National Health Interview Survey) The Bureau of Economic Analysis (BEA) provides historical personal income data from 1969, and makes projections on personal income by states through 2045. The GM Project G.6 4 - 2 October 2000
projections are for all age groups. Because of confidentiality and disaggregation issues, however, we were unable to use the BEA income projections. Census data sources contain historical long-term income data by various “types” of households (i.e., female head of household with no husband present, etc.), and by age groups. There are also historical data sets of median income by states for a “typical” family. Unfortunately, income data from the U.S. Census Bureau contain limited disaggregation capabilities. The NHIS is a large (about 40,000 households), well-designed, cross-sectional study, conducted annually, of non-institutionalized individuals. We considered using NHIS to derive income projections using either • Direct projection of elderly cohort-specific income, or • Separate projections of (a) non-elderly cohort-specific working income and (b) same cohort-specific decline in income during elderly years. Income data from the NHIS, however, had limited usage within the model because NHIS is not a longitudinal study, and income trends were difficult to project. Because we felt that projections of income were a critical component in the model, ORNL issued a subcontract to a reputable firm (Standard & Poor’s DRI) having experience in using population and income files from the U.S. Census Bureau to obtain income projections in the format and disaggregations desired. The DRI estimates and forecasts are explained in further detail in Appendix B.2.1.1. GM Project G.6 4 - 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
Page 16 and 17: Figure ES-1. Projected Active Drive
Page 18 and 19: Table ES-1. Elderly Driver Fatality
Page 20 and 21: 1. INTRODUCTION America is graying.
Page 22 and 23: of existing data bases, including h
Page 24 and 25: various data sources and their abil
Page 26 and 27: Finally, data sets should contain,
Page 28 and 29: • Examine data sources to determi
Page 30 and 31: health care, resulting in longer li
Page 32 and 33: Age group Table 3.2. Projected Rati
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
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 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 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
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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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GM Project G.6 8 - 13 October 2000
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GM Project G.6 8 - 15 October 2000
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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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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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