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Estimation of Vehicular Exposure at Highway-Rail Grade Crossings
Highway-rail grade crossings (HRGCs) are junctions where two modes of transportation intersect. More than 97% of HRGCs in the United States (US) are at the same level. While crashes at HRGCs are relatively uncommon, they tend to be more severe compared to crashes reported elsewhere on the surface transportation network. Therefore, there is a continuous need to make HRGCs safer. Statistical models are frequently used to estimate HRGC safety. In most HRGC safety models, vehicular exposure to crashes is an important factor. Annual average daily traffic (AADT) and daily train traffic (DTT) are the two most commonly used variables for vehicular exposure estimation. The use of AADT in HRGC safety models is questionable because the true crash exposure at an HRGC is a combination of train traffic and motor vehicles that arrive at that HRGC during train crossing events. This dissertation thus focuses on the following four objectives: 1) Investigate a better method for estimation of vehicular crash exposure at HRGCs, 2) Provide a traffic forecasting model to predict traffic count and variation at HRGCs, 3) Develop methods to estimate vehicular exposure at HRGCs to meet different research demands, and 4) Explore a rail crossing hazard ranking model with the estimated exposure. To fulfill these objectives, a preliminary study was conducted to show differences in safety rankings of HRGCs based on the usage of AADT and motor vehicle traffic present during train crossing events, called (AADT)TP in this research. Different ways of estimating (AADT)TP were investigated for safety assessments of HRGCs. Salient findings from this research include the following. Commonly used AADT in HRGC safety models provides biased safety rankings. The effects of influencing variables vary on roadway traffic and a spatial model should be considered when estimating roadway traffic. The three developed HRGC crash exposure estimation methods have their respective advantages and disadvantages in prediction accuracy, computational complexity, and data dependency. When building a crash frequency-based HRGC safety model, a zero-inflated Poisson (ZIP) model and its transformations should be considered.
Liu, Huiyuan, "Estimation of Vehicular Exposure at Highway-Rail Grade Crossings" (2020). ETD collection for University of Nebraska - Lincoln. AAI28085909.