Assessment of the Long-Term Impacts of Highway–Railway Grade Crossing Countermeasures: A Bayesian Vector Autoregression Modeling Approach

This paper proposes integrated machine learning and time-series models to investigate the long-term impact of a variety of safety countermeasures on the consequence score of train-vehicle collisions at highway–railway grade crossings (HRGCs). To that end, train-vehicle collisions that occurred at HRGCs in the United States between 2009 and 2018 were extracted from the US Federal Railroad Administration (FRA) collision dataset, along with the countermeasures that are implemented at each HRGC location. The consequence score of each collision was extracted from a web-based platform named GradeDec.Net, administrated by the FRA. A nonlinear M5Prime (M5P) model tree was developed to classify the collision dataset into a set of homogeneous classes based on the characteristics of the HRGCs (namely, train speed, highway speed, and traffic volume). In total, the HRGCs considered in the study were classified into five classes based on the results of the M5P model. Then, a Bayesian vector autoregression (BVAR) model was developed for each class to understand the temporal trends of the safety impact of eight countermeasures on the consequence score of collisions. The study showed that the impact of several countermeasures fluctuates over time depending on the prevailing conditions of the HRGCs. Some countermeasures showed negative short-term impacts in some classes, but in the long run, their safety benefits become evident. Other countermeasures showed limited short-term benefits but in the long run, their safety benefits deteriorate significantly. Moreover, the forecasting accuracy of the proposed BVAR model was evaluated by comparing the model predictions to the observed consequence score in the three years following the period used to train the model (2019–2021).