A Comprehensive Railroad-Highway Grade Crossing Consolidation Model: A Machine Learning Approach

Abstract In the United States, there are approximately 212,000 highway-rail grade crossings, some of which experience vehicle-train incidents that often cause a massive financial burden, loss of life, and injury. In 2017, there were 2,108 highway-rail incidents resulting in 827 injuries and 307 fatalities nationwide. To eliminate collision risks, crossing grade separation and active alarm improvement are commonly used. Moreover, crossing closures are considered to be the most effective safety improvement program. While it may be very difficult, and in some cases impossible to close crossings, there are some incentive programs that facilitate the closure process. One of these programs is a working consolidation model that aims to determine crossing closure suitability. Using details of highway-rail grade crossings in the United States and applying an eXtreme Gradient Boosting (XGboost) algorithm, this paper proposes a data-driven consolidation model that takes into consideration a number of engineering variables. The results indicated an overall accuracy of 0.991 for the proposed model. In addition, the developed XGboost consolidation model reported the relative importance of the variables input to the model, offering an in-depth understanding of the model’s behavior. Finally, for the practical implementation of the model, a simplified version containing fewer variables was developed. A sensitivity analysis was performed considering the aggregate gain and the different correlation threshold values between variables. This analysis developed a simplified model utilizing 14 variables, with aggregated gain values of 75% and a correlation threshold of 0.9 which would perform similarly to the full model. Based on this model, 62% of current highway-rail grade crossings should be closed.