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Page 96
Suggested Citation:"Chapter 9. Limitations." 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 96
Page 97
Suggested Citation:"Chapter 9. Limitations." 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 97

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Page 81 Chapter 9. Limitations The present study established associations between the independent variables and the outcome (traffic fatalities) but not the causal relationships. A series of mechanisms were suggested (see Table 8-1), linking the factors to their expected effects. Many of the findings were consistent with those mechanisms, but a truly causal linkage would require a different approach. Moreover, this was an observational, not experimental, study. Thus, there was no control over the explanatory factors to directly test their effect on the outcome (traffic fatalities). The “experiment” was, in effect, the Great Recession, which drastically affected certain of the transient variables (unemployment rates, GDP, household income), but had much less effect on more stable factors not directly connected to the economy (belt use rates, DUI laws). Most of the parameters used in the models were surrogates for factors that could not be directly measured. The general analytical explanatory framework was that a variety of economic pressures changed the distribution of who drives, how much they drive, and where they drive. However, there was relatively little data that bear directly on each of those points. For example, the unemployment rate, particularly for teens and young adults, was substantially associated with the decline in traffic fatalities. It was plausibly suggested that the reduction in employment caused a decline in discretionary and leisure travel by teens and young adults, but there are no comprehensive data showing that occurred over the period. There is evidence from the NHTS showing a substantial drop in self-reported travel by younger drivers, including teens, in 2009. But in the period covered, only two snapshots were available: one in 2001 at the beginning of the period and the other in 2009, which was within the recession period. In addition, it is likely that the decline in median household income constrained the driving of lower income groups, but there is no direct evidence (however, see (Maheshri and Winston 2015) using insurance data on a sample of drivers from Ohio, showing an association between aggregate unemployment rates and reduction of VMT of individuals in their sample). There is a need for much more granular exposure data; for example, data series on VMT by driver age or VMT by household income. The data showed significant variability across states, and the independent variables in the models did not capture all the variation between states. There are clearly other variables reflective of state differences that are not in the models. The fact that many of the parameter coefficients were stable between the MNCS and MCS models shows that the operation of those variables was stable across states. However, it is clear that some state-to-state differences were not captured. Another source of variability that complicated results was the sheer variability in size across states. The object of analysis was traffic fatalities, for which the counts ranged per year from 55 to 4,333. Extending the analysis to less severe crash types would increase data available and reduce relative variability. However, less-severe injuries are not as well-defined or measured as fatalities.

Page 82 Finally, there was a substantial amount of variability not accounted for in the models. Clearly, there are other factors that affected the number of traffic fatalities not included in the models. The next section addresses some areas of future research that may strengthen the results presented here.

Next: Chapter 10. Future research and data needs »
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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