Quantifying the Extent to Which Connected and Autonomous Vehicles Reduce Accidents at Railroad Grade Crossings: A Machine Learning Approach

Abstract

This paper quantifies the safety benefits of a proposed near real-time traffic control system for highway–rail grade crossing (HRGC) utilizing emerging safety technologies in connected and autonomous vehicles (CAV). The connected-vehicle technologies that have applications at a railroad crossing include vehicle-based technologies (railroad crossing violation warning, automated or semi-automated braking system, drowsiness/distracted driver alert) and technologies that require cooperation from the railroad industry (advanced warnings to trains about an occupied crossing). This paper provides a methodology to quantify the reduction in crashes as safety technologies become prevalent. It first identifies crash characteristics that enable the classification of crashes as preventable crashes or not. This classification is used to train machine learning models to estimate the likelihood of a potential crash being preventable. The machine learning model is used along with the zero inflated negative binomial with empirical Bayes system (ZINEBS) model to estimate the expected accident count at a crossing when a percentage of vehicles in the traffic stream is CAV. This paper presents case studies for three crossings to show the reduction in crashes with the increase in the percentage of connected vehicles in the traffic stream. It also presents the general trend in the reduction expected by analyzing 50 crossings of each warning device type.