Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations

Abstract

Safe railway operation is vital for public safety, the environment, and property. Concurrent with climbing amounts of rail traffic on the Canadian rail network are increases in the last decade in the annual crash counts for derailment, collision, and highway railroad grade crossings (HRGCs). HRGCs are important spatial areas of the rail network, and the development of community areas near railway tracks increases the risk of HRGC crashes between highway vehicles and moving trains, resulting in consequences varying from property damage to injuries and fatalities. This research aims to identify major factors that cause HRGC crashes and affect the severity of associated casualties. Using these causal factors and ensemble algorithms, machine learning models were developed to analyze HRGC crashes and the severity of associated casualties between 2001 and 2022 in Canada. Furthermore, spatial autocorrelation and optimized hotspot analysis tools from ArcGIS software were used to identify hotspot locations of HRGC crashes. The optimized hotspot analysis shows the clustering of HRGC crashes around major Canadian cities. The analysis of cluster characteristics supports the results obtained for causal factors of HRGC crashes. These research outcomes help one to better understand the major causal factors and hotspot locations of HRGC crashes and assist authorities in implementing countermeasures to improve the safety of HRGCs across the rail network.