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Page 27
Suggested Citation:"Chapter 5. Data." National Academies of Sciences, Engineering, and Medicine. 2019. Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012. Washington, DC: The National Academies Press. doi: 10.17226/25590.
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Suggested Citation:"Chapter 5. Data." National Academies of Sciences, Engineering, and Medicine. 2019. Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012. Washington, DC: The National Academies Press. doi: 10.17226/25590.
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Suggested Citation:"Chapter 5. Data." National Academies of Sciences, Engineering, and Medicine. 2019. Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012. Washington, DC: The National Academies Press. doi: 10.17226/25590.
×
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Page 30
Suggested Citation:"Chapter 5. Data." National Academies of Sciences, Engineering, and Medicine. 2019. Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012. Washington, DC: The National Academies Press. doi: 10.17226/25590.
×
Page 30
Page 31
Suggested Citation:"Chapter 5. Data." National Academies of Sciences, Engineering, and Medicine. 2019. Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012. Washington, DC: The National Academies Press. doi: 10.17226/25590.
×
Page 31
Page 32
Suggested Citation:"Chapter 5. Data." National Academies of Sciences, Engineering, and Medicine. 2019. Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012. Washington, DC: The National Academies Press. doi: 10.17226/25590.
×
Page 32

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Page 12 Chapter 5. Data Data series were assembled to cover the vehicle, driver, and environmental factors identified as related to traffic safety. In addition, numerous economic series were collected to attempt to reflect the influence of the economy on traffic safety. This section provides a list and discussion of the data and sources used. The data series collected were at the state level, meaning that the data were collected for each state and year within the period 2001 through 2012. However, data were not available at the state level on vehicle fleets. Accordingly, data on vehicle characteristics at the national level were used in the models. However, unless otherwise identified, all data series are by year and state. The data included all 50 states. The District of Columbia was excluded because it introduced excessive variance and only accounted for 0.1% of traffic fatalities in the period. 5.1 Crash data The National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS) is the standard source for data on fatal traffic crashes in the U.S. FARS provides a census file of all motor vehicle crashes in the U.S. that occurred on a trafficway customarily open to the public, and in which one or more persons died of crash injuries within 30 days of the crash. The FARS data set is composed of data compiled by analysts who are housed within each state. Data elements cover crash- level, vehicle-level, and person-level information. The data are collected from police accident reports, death certificates, vehicle registration files, hospital and coroner records, emergency medical service (EMS) reports, state highway department data, and other state records. There is one record for each crash, vehicle, and person involved in a fatal crash (NCSA 2014). FARS data were used for all analysis of fatal traffic crashes in this report. NHTSA’s General Estimates System data were used where it was informative to examine crashes of all severities. GES is a nationally-representative probability sample of police-reported crashes in the U.S. Crashes are sampled by a stratified, hierarchical sampling system, from about 400 police jurisdictions nationally. About 50,000 crash reports are sampled each year. All data in GES were coded from police reports, without any additional investigation. The variables and code levels are largely consistent with variables and code levels in FARS (NHTSA 2014). GES data are sampled through a national sampling structure, and cannot be used to form state-level estimates. 5.2 Sources of other data used Exposure includes all types of measures that reflect the opportunity or exposure to the possibility of a crash. Table 5-1 lists the primary sources of exposure data. Population data are available from the Bureau of the Census, for each state, including counts by state, age cohort, and year. Years between the census years of formal counts are estimated by interpolation. The Bureau of Census also has estimates of state

Page 13 area, which were used to compute population density. Road miles by FHWA function classes were used to normalize state highway expenditures, in order to control for differences in the sizes of states. VMT or vehicle miles traveled is probably the most important measure of exposure used, because it most directly captures exposure to crashes. The FHWA Highway Statistics publication provides annual estimates by roadway function class (including urban and rural) and vehicle type. Highway Statistics also includes vehicle registration data by year and state, as well as many other relevant data series, which will be discussed below (FHWA 1992-2014). Table 5-1 Exposure data series Data  Source  Population by state &  age  Bureau of the Census, Table 2. Intercensal Estimates of the  Resident Population by Sex and Age.  Square miles by state  Bureau of the Census, Geography, accessed at https://www.census.gov/geo/reference/state‐area.html  Road miles by roadway  function class and year  Highway Statistics, Federal Highway Administration (FHWA),  Table hm10 for each year, 2001‐2012.  VMT by roadway  function class, vehicle  type, urban/rural,  national  Highway Statistics, FHWA, Table VM‐1 for each year, 2001‐2012.  VMT by roadway  function class,  urban/rural, by state  Highway Statistics, FHWA, Table VM202 for each year, 2001‐ 2012.  Vehicle registrations by  type and state  Highway Statistics, FHWA, Table MV‐1 for each year, 2001‐2012.  Data series on employment, the labor force, and unemployment were obtained from the Bureau of Labor Statistics, based on the Current Population Survey (Table 5-2). They are available by year and month for each state; annual state-level estimates were obtained by summing across the employment and labor force counts and taking the average. Employment was defined as the total number of persons on establishment payrolls employed full or part time who received pay for any part of the pay period which includes the 12th day of the month. Unemployed persons were defined as all persons 16 or older who had not employment, were available for work, and had made specific efforts to obtain employment. The labor force was defined as all persons either employed or unemployed according to those definitions. The full definitions can be obtained here: http://www.bls.gov/sae/790faq2.htm#Ques3.

Page 14 Table 5-2 Economic data series Data  Source  Employment, total counts  of employed by state,  month, & year  Bureau of Labor Statistics, Current Population Survey, Local Area  Unemployment Statistics.  Labor force, by state,  month & year  Bureau of Labor Statistics, Current Population Survey  Unemployment rate, by  state, month, & year  Bureau of Labor Statistics, Current Population Survey  State GDP by year  US Department of Commerce, Bureau of Economic Analysis, Regional Economic Accounts: Download  State median household  income by year  US Census Bureau, Current Population Survey, Annual Social and  Economic Supplements.  Fuel tax by state by year  Highway Statistics, FHWA. Table MF‐205.  Fuel costs  US Energy Information, State Energy Data System, prices for  regular gasoline, data are converted from prices per million  BTUs.  Gross domestic product (GDP) estimates by state and year were obtained from the US Department of Commerce. GDP measures the gross productive output of a state, so it is used as a gross estimate of economic activity. The estimates were divided by population estimates to produce GDP per capita estimates. Median household income estimates were obtained from the Bureau of Census Current Population Survey. The estimates available were for two- to three-year periods, not for individual years. Estimates for individual years were obtain by averaging over spans of years. For example, to obtain an estimate for 2010, estimates for 2009-2010 and 2010-2011 were averaged. Household income combines all incomes within a household, while GDP/capita is on a per person basis. All monetary estimates were converted to constant 2013 dollars using the CPI (Consumer Price Index) calculator at the Bureau of Labor Statistics (Bureau of Labor Statistics 2013). Fuel prices were obtained from the US Energy Information Administration, State Energy Data System. Prices for regular-grade gasoline were selected for fuel prices, since they represent the most common grade of fuel used. The prices were converted from prices per million BTUs to gallons, and then converted to constant 2013 dollars. Fuel taxes, in terms of cents per gallon, were available in the FHWA’s Highway Statistics Series, Table MF-205, which tabulates fuel taxes for each state. Again, tax values were converted to constant 2013 dollars and summed with the fuel cost to produce an estimate of the price at the pump. The Insurance Institute for Highway Safety (IIHS) maintains a valuable set of digests of state laws respecting critical aspects of traffic safety. These data were used to develop the indexes on the strength of state belt laws and motorcycle helmet requirements. Belt use rates are available from the continuing National Occupant Protection Use Survey (NOPUS), published annually by NHTSA. The National

Page 15 Institute of Alcohol Abuse and Alcoholism publishes estimates of per capita consumption of beer, wine, and alcoholic spirits. These are available by state and year. Kathleen Klinich of UMTRI has been compiling state laws related to drunk driving and kindly shared data that were used to develop on index of state penalties and regulations. ESC penetration rates were estimated from a Highway Loss Data Institute report. And finally, the penetration of post-1991 model year vehicles into the fleet was estimated using quasi-induced exposure methods. The rate of penetration was used as a surrogate for the spread of more crashworthy vehicles, in response to NHTSA’s New Car Assessment Program and the strengthening of the Federal Motor Vehicle Safety Standards. Table 5-3 Driver- and vehicle-related framework Data  Source  Seat belt, primary  vs secondary, by  state and year  Compiled from Insurance Institute for Highway Safety, digest of state laws, available at  http://www.iihs.org/iihs/topics/laws/safetybeltuse.  Belt use rates  Compiled from NHTSA’s NOPUS program, reported in (Chen and Ye 2009; Chen 2014)  BAC limit, per se,  other alcohol  related laws and  penalties, by state  by year  Compiled from state laws, index developed from (Klinich 2016)  Motorcycle helmet  by state by year  Digest of motorcycle helmet laws from IIHS website, accessed at  http://www.iihs.org/iihs/topics/laws/helmetuse/helmethistory?  topicName=Motorcycles#tableData  Alcohol  consumption  Compiled from National Institute of Alcohol Abuse and Alcoholism, (Haughwout, LaVallee  et al. 2015)  ESC‐penetration  Compiled from Highway Loss Data Institute report on the penetration of collision avoidance technologies, (Highway Loss Data Institute 2014)  Post1991 model  year  Estimated from GES, using a quasi‐induced exposure technique.   Data series on state highway expenditures are available in the FHWA’s Highway Statistics series. This series is an exceedingly valuable resource for highway safety. Each year, states report highway spending disaggregated by several types of activities, using a set of common forms, definitions, and instructions. Funding under the Highway Safety Improvement Program (HSIP) was compiled from FHWA funding tables under SAFETEA-LU (2005) and MAP-21 (2012), which are available on the FHWA website (see Table 5-4).

Page 16 Table 5-4 Highway expenditures Data  Source  Capital  expenditures  Compiled from Highway Statistics, FHWA, Table SF‐2, includes construction, relocation,  resurfacing, restoration, rehabilitation and reconstruction, widening, capacity improvements,  restoration of failed components, additions and betterments of roads and bridges. See  (Federal Highway Administration N.D.)  Maintenance  Compiled from Highway Statistics, FHWA, Table SF‐2, includes preserving the entire highway,  including surface, shoulders, roadsides, structures, and traffic control devices, as close as  possible to the original condition as designed and constructed.  Administration,  research,  planning  Compiled from Highway Statistics, FHWA, Table SF‐2, including all general and miscellaneous  expenditures not related to a specific project, expenditures for highway planning, research,  and planning  Law  enforcement  and safety  Compiled from Highway Statistics, FHWA, Table SF‐2, including all relevant Federal Safety  programs, sections 402, 403, 405,406, 407, 408, 410, and 411 of Title 23 of the United State  Code, as well as MCSAP. Also includes capital expenditures designated by states as safety‐ related.  Highway Safety  Improvement  Program  Compiled from FHWA funding tables under SAFETEA‐LU and MAP‐21, available from  https://www.fhwa.dot.gov/safetealu/fundtables.htm and  https://www.fhwa.dot.gov/map21/funding.cfm  Highway spending was used in the statistical models to capture the effect of infrastructure and state highway programs on safety. Clearly, highway spending is an imperfect surrogate because the cost- benefit ratio of projects differ. However, it is believed that this surrogate is the best currently available. There are evaluations of specific projects, and crash modification factors (CMF) have been developed for different types of projects, e.g., (AASHTO ; AASHTO 2010). But there are no comprehensive data to translate CMFs into variables that capture the effect of modifications in a system-wide fashion. For example, there is an ample literature evaluating the safety effect of installing rumble strips on shoulders and centerlines, but no comprehensive data on the penetration of rumble strips into the roadway system. Moreover, a safety-related spending variable was constructed that aggregates all spending states themselves identified as safety-related: law enforcement, state educational safety programs, and the portion of capital spending that the states declared to be safety-related. Finally, it is assumed that state departments of transportation attempted to deploy their resources effectively. There are no doubt variations in effectiveness, but in light of currently available data, highway spending should be a reasonable approximation.

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Next: Chapter 6. Trends in contributing factors »
Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012 Get This Book
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Between 2005 and 2011, the number of traffic fatalities in the U.S. declined by 11,031, from 43,510 in 2005 to 32,479 in 2011. This decline amounted to a reduction in traffic-related deaths of 25.4 percent, by far the greatest decline over a comparable period in the last 30 years.

Historically, significant drops in traffic fatalities over a short period of time have coincided with economic recessions. Longer recessions have coincided with deeper declines in the number of traffic fatalities. This report from the National Cooperative Highway Research Program, NCHRP Research Report 928: Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012, provides an analysis that identifies the specific factors in the economic decline that affected fatal crash risk, while taking into account the long-term factors that determine the level of traffic safety.

A key insight into the analysis of the factors that produced the sharp drop in traffic fatalities was that the young contributed disproportionately to the drop-off in traffic fatalities. Of the reduction in traffic fatalities from 2007 to 2011, people 25-years-old and younger accounted for nearly 48 percent of the drop, though they were only about 28 percent of total traffic fatalities prior to the decline. Traffic deaths among people 25-years-old and younger dropped substantially more than other groups. Young drivers are known to be a high-risk group and can be readily identified in the crash data. Other high-risk groups also likely contributed to the decline but they cannot be identified as well as age can.

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