Prediction of Fatalities at Northern Indian Railways’ Road–Rail Level Crossings Using Machine Learning Algorithms

Abstract

Highway railway level crossings, also widely recognized as HRLCs, present a significant threat to the safety of everyone who uses a roadway, including pedestrians who are attempting to cross an HRLC. More studies with new, proposed solutions are needed due to the global rise in HRLC accidents. Research is required to comprehend driver behaviours, user perceptions, and potential conflicts at level crossings, as well as for the accomplishment of preventative measures. The purpose of this study is to conduct an in-depth investigation of the HRLCs involved in accidents that are located in the northern zone of the Indian railway system. The accident information maintained by the distinct divisional and zonal offices in the northern railways of India is used for this study. The accident data revealed that at least 225 crossings experienced at least one incident between 2006 and 2021. In this study, the logistic regression and multilayer perception (MLP) methods are used to develop an accident prediction model, with the assistance of various factors from the incidents at HRLCs. Both the models were compared with each other, and it was discovered that MLP supplied the best results for accident predictions compared to the logistic regression method. According to the sensitivity analysis, the relative importance of train speed is the most important, and weekday traffic is the least important.