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ix Summary Objective The research objective, as outlined in the original Request for Proposal, was to âprovide a multidisciplinary analysis of the relative influence of the types of factors that contributed to the recent national decline in the number of highway fatalities and rates in the United States.â Between 2005 and 2011, peak to trough, 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 by 25.4%, by far the greatest decline over a comparable period in the last 30 years. Figure S-1 Traffic fatalities, 2001-2012 Historically, significant drops in traffic fatalities over a short period of time have coincided with economic recessions. Figure S-2 displays the number of traffic fatalities by year from 1966 through 2016, along with the periods of the seven recessions during that span. (Traffic fatalities for 2016 were projected from an early estimate by the National Highway Traffic Safety Administration (NHTSA) of the number of fatalities during the first half of 2016 (NHTSA 2016a).) Longer recessions have coincided with deeper declines in the number of traffic fatalities. This project 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.
x Figure S-2 Motor vehicle traffic fatalities and periods of recession, 1966-2012 The fundamental approach of this research project was based on the understanding that the number of fatalities and injuries in crashes is the product of risk times exposure: Fatalities = Risk Ã Exposure Eq. S-1 Figure S-3 displays the relationship between VMT (exposure), traffic fatalities (outcomes), and traffic fatality rates by VMT (risk), over the period. The figure shows the ratio of the values for each year to the base year of 2001. Exposure as measured by VMT was relatively stable. However, a reduction in the risk of travel pulled down the number of traffic fatalities. Thus, the fatality risk of travel contributed significantly to the substantial decline in fatalities over the period. The decrease in exposure due to the recession and subsequent slowdown in economic activity contributed less. The goal of this project was to identify the sources of reduced risk.
xi Figure S-3 Fatality rates by VMT and vehicle registrations, and fatalities, normalized to 2001 Analysis approach Factors that affected the incidence and risk of fatal crashes and fatal crash injuries over the period were organized using the Haddon Matrix. The Haddon Matrix provides a framework that covers the factors comprehensively (Williams 1999). The utility of the Haddon Matrix was to ensure that all components of what might be called the crash systemâvehicle, drivers, and environmentâwere considered. Fundamentally, two processes were at work over the period. The first process set the baseline level of safety that influenced the number of traffic deaths each year. The baseline level was the product of long- term trends in factors known to affect traffic safety, such as safety belt use, improvements in the crashworthiness of cars, highway infrastructure, traffic enforcement and safety campaigns, driver license laws, and other efforts to reduce the number of fatalities on U.S. roads. Most of these factors operated incrementally and changed relatively slowly over time. Highway infrastructure cannot change dramatically over a short period of time. Safety belt use is known to be a primary safety intervention, but belt use increased slowly and monotonically over the period. The second process at work over the period consisted of the factors that precipitated the sharp decline in fatal crashes and deaths in 2008-2011. The major event in this period was the recession that started in December 2007 and ended in June 2009 (NBER 2010). Of the components of the crash systemâvehicle, drivers, and environmentârecessions have a short-term and substantial impact on drivers. Moreover, the recession affected some high-risk groups more than others, particularly younger drivers, so it may have taken some risky drivers off the road and also reduced the amount of risky driving. At the same time, the long-term factors that influenced safety continued, such as incremental increases in safety belt use, the penetration of more crashworthy passenger vehicles into the fleet, safety campaigns to improve driver behavior, infrastructure improvements, and other factors. Explaining the drop in traffic
xii fatalities between 2008 and 2011 was a major goal of the project, but the explanation is undertaken within the context of overall trends in traffic safety over the period. Findings and statistical models 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, persons less than 26 years of age accounted for almost 48% of the drop, though they were only about 28% of total traffic fatalities prior to the decline. Figure S-4 shows that traffic deaths among persons 25 years-old or less 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. Figure S-4 Ratio of traffic fatalities by age groups, normalized to 2001 Using the Haddon Matrix and the research teamâs broad expertise, a comprehensive set of parameters were identified to evaluate their contribution to the drop in traffic fatalities after 2007. Statistical models of the incidence of traffic fatalities over the period were developed using these parameters. Table S-1 provides the expected association of the various parameters in the models and describes the mechanisms through which they were hypothesized to affect the number of traffic fatalities. Most of the modeling results were consistent with these expectations, though not all.
xiii Table S-1 Explanatory factors and expected mechanisms of activity VariableÂ ExpectedÂ associationÂ withÂ trafficÂ fatalitiesÂ ExpectedÂ mechanismÂ TotalÂ VMTÂ PositiveÂ IncreaseÂ inÂ VMTÂ increasesÂ exposureÂ toÂ trafficÂ crashesÂ andÂ thereforeÂ fatalities.Â ProportionÂ ruralÂ VMTÂ PositiveÂ IncreasedÂ proportionÂ ofÂ ruralÂ VMTÂ increasesÂ proportionÂ ofÂ travelÂ onÂ riskierÂ roads,Â leadingÂ toÂ moreÂ fatalities.Â PumpÂ priceÂ NegativeÂ IncreasedÂ pumpÂ priceÂ raisesÂ costÂ ofÂ travel,Â reducingÂ totalÂ travelÂ andÂ discretionaryÂ travel,Â reducingÂ exposureÂ toÂ fatalÂ crashes.Â GDPÂ perÂ capÂ PositiveÂ GDPÂ perÂ capitaÂ reflectsÂ economicÂ activityÂ whichÂ inÂ turnÂ leadsÂ toÂ moreÂ travel,Â moreÂ exposureÂ toÂ crashes,Â andÂ moreÂ fatalities.Â MedianÂ IncomeÂ PositiveÂ IncreasedÂ medianÂ incomeÂ increasesÂ discretionaryÂ andÂ leisureÂ travel,Â resultingÂ inÂ moreÂ exposureÂ andÂ moreÂ fatalities.Â 16â24Â UnemploymentÂ NegativeÂ IncreasedÂ unemploymentÂ reducesÂ totalÂ travelÂ andÂ discretionary,Â leisureÂ travel,Â resultingÂ inÂ fewerÂ fatalities.Â CapitalÂ spend/mileÂ (lag)Â MixedÂ ImprovedÂ infrastructureÂ wouldÂ beÂ expectedÂ toÂ shiftÂ travelÂ toÂ higherÂ qualityÂ roads.Â ItÂ mayÂ alsoÂ induceÂ moreÂ travel,Â thusÂ moreÂ exposureÂ toÂ fatalities.Â SafetyÂ spend/mileÂ (lag)Â NegativeÂ IncreasedÂ trafficÂ enforcement,Â education,Â andÂ safetyÂ programsÂ wouldÂ reduceÂ riskyÂ drivingÂ andÂ reduceÂ fatalities.Â BeltÂ useÂ rateÂ NegativeÂ IncreasedÂ beltÂ useÂ providesÂ moreÂ protectionÂ toÂ vehicleÂ occupantsÂ andÂ reducesÂ theÂ probabilityÂ ofÂ fatalÂ injury,Â givenÂ aÂ crash.Â DUIÂ lawÂ ratingÂ NegativeÂ IncreasedÂ stringencyÂ ofÂ DUIÂ lawsÂ reducesÂ drunkÂ (risky)Â drivingÂ andÂ trafficÂ fatalities.Â MotorcycleÂ helmetÂ lawÂ ratingÂ NegativeÂ IncreasedÂ stringencyÂ ofÂ motorcycleÂ helmetÂ lawsÂ providesÂ moreÂ protectionÂ toÂ motorcycleÂ ridersÂ andÂ reduceÂ theÂ probabilityÂ ofÂ fatalityÂ givenÂ aÂ crash.Â BeerÂ consumptionÂ PositiveÂ IncreasedÂ beerÂ consumptionÂ mayÂ increaseÂ drivingÂ whileÂ underÂ theÂ influenceÂ ofÂ alcohol,Â increaseÂ riskyÂ driving,Â andÂ increaseÂ trafficÂ fatalities.Â WineÂ consumptionÂ PositiveÂ IncreasedÂ wineÂ consumptionÂ mayÂ increaseÂ drivingÂ whileÂ underÂ theÂ influenceÂ ofÂ alcohol,Â increaseÂ riskyÂ driving,Â andÂ increaseÂ trafficÂ fatalities.Â PenetrationÂ ofÂ modelÂ yearÂ >1991Â NegativeÂ IncreasedÂ penetrationÂ ofÂ vehiclesÂ thatÂ provideÂ moreÂ occupantÂ protectionÂ andÂ moreÂ safetyÂ featuresÂ reducesÂ theÂ probabilityÂ ofÂ aÂ crash,Â andÂ reducesÂ theÂ probabilityÂ ofÂ fatalÂ injuryÂ givenÂ aÂ crash.Â Two basic approaches were used to model the factors that were associated with the drop in traffic fatalities after 2007. The first approach was a set of count models, using negative binomial models to examine the associations between predictors (the variables in the table above) and raw fatality counts. Two count models were developed. One used a state fixed effect to remove the stable differences between
xiv states and focus on changes over time (labelled the model considering state or MCS model). The other left out this fixed effect, allowing differences between states to be captured by the measured predictors (model not considering state (MNCS)). The other statistical modeling technique was a log-change regression model, to model the association between the change in predictor variables in one year with the change in the outcome variable (traffic fatalities) in the following year. Table S-2 shows the results from the two count models. Table S-3 provides the results from the change model. Table S-2 Effects of Count Model Factors VariableÂ ChangeÂ inÂ ParameterÂ ValueÂ fromÂ 2007Â toÂ 2011Â MNCSÂ modelÂ MCSÂ modelÂ PredictedÂ ChangeÂ inÂ FatalitiesÂ StatisticallyÂ SignificantÂ atÂ 5%Â level?Â PredictedÂ ChangeÂ inÂ FatalitiesÂ StatisticallyÂ SignificantÂ atÂ 5%Â level?Â RuralÂ VMTÂ ProportionÂ asÂ percentÂ ofÂ totalÂ â0.8Â % â103Â YesÂ 95Â YesÂ StateÂ GrossÂ DomesticÂ ProductÂ perÂ CapitaÂ â$6301Â perÂ personÂ â617Â Yes â1236Â YesÂ UnemploymentÂ RateÂ forÂ 16Â toÂ 24Â yearÂ oldsÂ +6.39% â3305Â Yes â3125Â YesÂ PumpÂ priceÂ +$0.55/gallonÂ â877Â NoÂ 127Â NoÂ PerÂ CapitaÂ BeerÂ ConsumptionÂ â0.08Â Gal./personÂ â835Â Yes â1312Â YesÂ MedianÂ income â$3760Â 2677Â Yes â466Â YesÂ DUIÂ lawsÂ rating â1.05 â120Â No â261Â YesÂ SafetyÂ BeltÂ lawsÂ rating â0.16Â 5Â No â28Â NoÂ MotorcycleÂ helmetÂ lawÂ ratingÂ 0Â 0Â YesÂ 0Â NoÂ Table S-3 Effects of change-model predictors for 2007-2011 VariableÂ 2007Â MeanÂ 2011Â MeanÂ PercentÂ changeÂ inÂ predictorÂ 2007â2011Â PercentÂ changeÂ inÂ predictedÂ fatalitiesÂ Â 2007â2011Â TotalÂ VMTÂ 3,031,124Â 2,962,740 â2.3% â1.2%Â ProportionÂ ruralÂ VMTÂ 0.33Â 0.32 â1.6% â0.1%Â PumpÂ priceÂ changeÂ 3.11Â 3.20Â 2.6% â0.1%Â GDPÂ perÂ capÂ changeÂ 59,687Â 54,519 â7.5% â1.2%Â MedianÂ incomeÂ changeÂ 56,081Â 53,621 â4.3% â2.2%Â 16â24Â unemp.Â changeÂ 10.59Â 16.69Â 55.7% â6.1%Â
xv VariableÂ 2007Â MeanÂ 2011Â MeanÂ PercentÂ changeÂ inÂ predictorÂ 2007â2011Â PercentÂ changeÂ inÂ predictedÂ fatalitiesÂ Â 2007â2011Â CapitalÂ spend/mileÂ (lag)Â changeÂ 73.69Â 81.27Â 7.9% â0.1%Â SafetyÂ spending/mileÂ (lag)Â changeÂ 13.61Â 14.68Â 9.3%Â 0.1%Â BeltÂ useÂ rateÂ changeÂ 85.77Â 88.10Â 2.4% â0.1%Â DUIÂ lawÂ ratingÂ changeÂ 19.77Â 20.50Â 4.0% â0.7%Â MotorcycleÂ helmetÂ lawÂ ratingÂ changeÂ 2.91Â 2.91Â 0.0%Â 0.0%Â BeerÂ consumptionÂ changeÂ 1.21Â 1.15 â3.5% â0.7%Â WineÂ consumptionÂ changeÂ 0.37Â 0.39Â 5.0% â0.1%Â MY>1991Â changeÂ 95.80Â 97.11Â 1.4%Â 0.1%Â The two modeling approaches were in broad agreement. The most significant contributors to the drop in traffic fatalities after 2007 were the substantial increase in teen and young adult unemployment, the reductions in median household income, and the reduction GDP/capita income. The right-most column in Table S-3 estimates the percentage decline contributed by each factor in the change model. The decline in rural VMT, increased strictness of DUI laws, and decreased beer consumption also contributed. State highway spending was not a significant contributor to the drop; the effect of changes in infrastructure was likely more cumulative and longer term. Changes in safety belt use rates and fuel prices were not significant contributors to the decline in traffic fatalities after 2007 because they did not change much over the period. It should be noted that failing to find that certain well-established safety interventions (safety-belt usage, highway capital improvements) did not contribute significantly to the sharp drop in traffic fatalities during the recession does not mean that they are not essential tools to reduce traffic fatalities. It means that their impact was not detectable given the magnitude of the short-term effects of other factors. The long-term factors that set the baseline of traffic safety continued to operate. Overlaid on top of them was the short term shock of the recession that drove up unemployment, particularly among teens and young adults, and declining median income that likely reduced driving and risky driving among high-risk populations. Implications and further research ï· Teens and young adults contributed disproportionately to the reduction in traffic fatalities 2008 through 2011. It is suggested here that the mechanism was economic constraints reduced total travel and risky (discretionary and leisure) travel. It has long been known that teens and young
xvi adults have disproportionately high crash risk, but the results from this study suggest that their behavior can be significantly modified over the short run, substantially reducing fatalities. ï· The findings related to median household income are consistent with an income effect. This finding warrants further investigation, but interventions aimed at lower income groups may have a disproportionately positive effect, similar to reducing crash risk among teens and young adults. ï· DUI laws showed a significant positive effect in reducing traffic fatalities, even over the short term of this study and even within the substantial impact of the economic contraction. Reduced beer consumption similarly showed a significant, positive effect. It is clear that continuing to focus on reducing drunk driving can have a disproportionate effect on reducing traffic fatalities. ï· Rural VMT bears a higher risk of fatal crashes across all road types; reduction in the proportion of rural VMT was significant in all models. Programs aimed at reducing the risk of rural travel can substantially reduce traffic fatalities. ï· It may be difficult to discern in any given year the effects of safety countermeasures, due to the significant influence of other factors on traffic fatalities. There is a need to more fully document and assess safety advances from countermeasures because these other factors may obscure them. ï· The results here clearly illustrate that factors outside the authority of safety professionals can have highly significant impacts on the level of highway safety. In the short term, shocks in the economy can overwhelm the effect of safety interventions that generally influence crash risk in the long term. It is clear that exogenous factors such as economic trends should be accounted for in setting realistic goals and evaluating traffic safety programs.