A Bayesian assessment of the effect of highway bypasses in Iowa on crashes and crash rate.

INTRODUCTION: A common contention is that the construction of highway bypasses negatively impacts the economy of local communities by reducing pass-by traffic for businesses. However, as access to specific business' account records is limited, this impact is difficult to quantify. Another common contention is that bypasses contribute to a reduction in overall crashes in the community and in the surrounding areas. Even though a large number of bypasses have been constructed in the State of Iowa over the past several years, their actual impact in terms of traffic safety has not been quantified. OBJECTIVES: This study seeks answers to the following questions: (a) Are bypasses in Iowa associated with a reduction in crash frequencies and crash rates on the bypassed highway? (b) Do bypasses in Iowa introduce a reduction of overall crash frequencies and rates or do they merely shift crashes from the highways through the communities to the bypasses with no significant overall reduction? METHOD: We obtained crash information from the Iowa DOT at 19 sites on which a bypass was constructed sometime during the past 23 years. We also obtained the same information at six sites used as comparison sites on which no bypasses were constructed at least until 2005. We them employed a Bayesian approach to estimating the association between the construction of the bypass and crash rates, while also accounting for other factors. RESULTS: The construction of bypasses in Iowa is associated with a significant increase in traffic safety both on the main road through town and on the combined main road and bypass roadway.

Teenage driver crash incidence and factors influencing crash injury by rurality.

BACKGROUND: Previous research has identified teenage drivers as having an increased risk for motor-vehicle crash injury compared with older drivers, and rural roads as having increased crash severity compared with urban roads. Few studies have examined incidence and characteristics of teen driver-involved crashes on rural and urban roads. METHODS: All crashes involving a driver aged 10 through 18 were identified from the Iowa Department of Transportation crash data from 2002 through 2008. Rates of overall crashes and fatal or severe injury crashes were calculated for urban, suburban, rural, and remote rural areas. The distribution of driver and crash characteristics were compared between rural and urban crashes. Logistic regression was used to identify driver and crash characteristics associated with increased odds of fatal or severe injury among urban and rural crashes. RESULTS: For younger teen drivers (age 10 through 15), overall crash rates were higher for more rural areas, although for older teen drivers (age 16 through 18) the overall crash rates were lower for rural areas. Rural teen crashes were nearly five times more likely to lead to a fatal or severe injury crash than urban teen crashes. Rural crashes were more likely to involve single vehicles, be late at night, involve a failure to yield the right-of-way and crossing the center divider. CONCLUSIONS: Intervention programs to increase safe teen driving in rural areas need to address specific risk factors associated with rural roadways. IMPACT ON INDUSTRY: Teen crashes cause lost work time for teen workers as well as their parents. Industries such as safety, health care, and insurance have a vested interest in enhanced vehicle safety, and these efforts should address risks and injury differentials in urban and rural roadways.

Evaluation of Iowa's graduated driver's licensing program.

The effectiveness of Iowa's graduated driver's licensing (GDL) program was evaluated for a 4-year period before and after implementation in 1999. Since some changes had occurred in the crash reporting format, changes in crash rates for younger drivers were compared to those for 35-44-year-old drivers (middle-age group of drivers) who were used as a control group. After implementation of GDL, the 14-, 16- and 17-year-old age groups experienced a greater decrease in crash rate than the middle-age control group while 15-year-old experienced a smaller decrease. This suggests that the crash rate for 15-year-old drivers may actually have increased when downward trends were adjusted for. Iowa's GDL program allows holders of the instruction permit to travel unaccompanied to and from school and school-endorsed activities after obtaining a minor school license. Fifteen-year-old with minor school licenses account for up to 26.7% of 15-year-old license holders yet represent up to 74.8% of 15-year-old drivers involved in crashes (depending on the year) from 1998 to 2004. As a result, 15-year-old drivers with minor school licenses are involved in 7.2-8.9 times more crashes, are 7.7 times more likely to have one or more sanctions, and are 4.8 times more likely to receive one or more moving convictions than their peers with a regular instruction permit. This help may explain why 15-year-old drivers did not seem to benefit from implementation of the GDL program in Iowa.

The choice of statistical models in road safety countermeasure effectiveness studies in Iowa.

With few exceptions, model selection in traffic safety studies does not receive as much attention as do the methods implemented to estimate the parameters in those models. In this manuscript, we focus on the modeling step in an intervention study and discuss issues associated with formulation, interpretation, comparison and selection of models for intervention studies. All of the statistical models we consider rely on an over-dispersed Poisson assumption for the crash densities, and are fitted by Bayesian methods. The crash data we use arose from a study by the Iowa Department of Transportation to evaluate the effectiveness of converting roads from four lanes to three lanes. Deviance and the deviance information criterion (DIC) are used for model selection. In the Iowa road diet study, a subset of best models (which fit the data better than others) was then also used to carry out posterior predictive checks to assess model fit.