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Crashes at highway rail grade crossings (HRGCs) are often involved with fatalities due to the momentum of a train. This study collected nine years (2010–2018) of fatal HRGC crashes from the Fatality Analysis Reporting System (FARS) to perform the analysis. The Taxicab Correspondence Analysis (TCA) was applied to this dataset. This method identified several patterns that trigger HRGC-related fatal crashes. The findings indicate that fatal crashes involving multiple fatalities are often highly associated with alcohol-influenced drivers, poor lighting conditions, and inclement weather. The fatal crash that occurs during the daylight with the uninfluenced driver is less likely to involve more than one fatality. The results also recognized the combinations of vehicle type and speed are associated with fatal crashes at rail grade crossings. The relatively low-speed limit crossings and larger utility vehicles are more likely to be associated with fatal crashes because large vehicles require a longer time to cross railroads at a low speed. The relatively high-speed limit crossing and smaller or lighter vehicles, especially the motorcycle, are highly associated with fatal crashes.

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making.

This study aims to rank high crash risk areas and measure the impacts of accessibility and other spatial and socio-economic factors on county-level crash severities while controlling for spatial and temporal unobserved effects. Among other insights, the results suggest that primary roadway density is positively associated with all crash severities. Contrary, intersection density is negatively associated with injury and no injury crashes. Furthermore, school density and rates of driving under the influence are both positively associated with no injury crashes. Additionally, the mean travel time to the hospital is positively associated, while urbanity is negatively associated with fatal crashes. Unobserved effects due to unstructured random effect terms are significant and contribute to the model’s overall goodness of fit. The within-area correlation among crash severities is positive and non-negligible. From a temporal perspective, both fatal and no injury crashes have decreasing trends. However, no clear pattern for injury crashes is observed across the years. From a spatial perspective, extreme high and low crash risk counties are identified throughout the study area with no clear observed patterns of clustering or dispersion.

The recent development of Automated Traffic Signal Performance Measures (ATSPMs), has provided new opportunities and insights into traffic signal operations. As agencies begin to make decisions regarding investment in infrastructure and operation systems, it is imperative to understand the impacts these systems may have on safety. Past research has thoroughly investigated the impact of geometry and signal timing parameters on the safety of intersections, but little is understood on the relationship between improved signal performance and safety. This study uses vehicle trajectory data to create performance metrics for 121 signalized intersections on ten corridors near Columbus, Ohio. These metrics are used to understand the relationship between signal performance and safety. Two performance metrics, percent arrivals on green (POG) and level of travel time reliability (LOTTR), were used along with other volume and geometric data to model the total crash frequency on signalized mainline approaches. The crash data were modeled using a random parameters negative binomial approach. In consideration of potential unobserved heterogeneity between intersections, a correlated random parameters specification was tested alongside the traditional uncorrelated random parameters and fixed parameters model. Based on goodness of fit measures, the correlated random parameter model was chosen to interpret results because this model explains the complex cross-correlation among the estimates of random parameters. The elasticity values revealed a one percent increase in percent arrivals on green is associated with a reduction in total crashes by 1.12 %. The results of this study show the investment in signal operations and optimization result in an improvement in safety at signalized intersections. Further research should be explored to expand this study to additional intersections over a larger time period.

The evaluation of driver behavior in dilemma zones is of critical importance to traffic safety and intersection design, but has thus far received limited investigation. Internal and external factors that can influence this behavior include driver demographics, the use of technology in the vehicle, and even the type of dilemma zone—Type I (based on signal timing/intersection geometry) or Type II (based on driver indecision). This paper explores the effect of cell phone technology, calling behavior, and driver characteristics on drivers' decisions to stop or go through an intersection at the onset of a yellow light, along with their corresponding rate of acceleration. A discrete/continuous analysis was adopted that accounted for potential model heterogeneity that may exist across the different dilemma zone types and drivers by applying selectivity-bias corrections. The results suggested that several technology and call behavior combinations are relevant in determining driver behavior, along with gender and age. It was also found that significant differences exist in driver behavior amongst those who decide to stop and those who decide to go through the intersection in a Type II dilemma zone situation. The findings of this paper can support efforts on reducing risky driving behavior in dilemma zones and enforcing distracted driving laws.