Derailment Prediction Models for Canadas Rail Network

Train related accidents, particularly derailments, can lead to severe consequences especially when they involve injuries or fatalities or involve dangerous goods that might result in environmental impacts. A literature review found that rail safety assessment and derailment prediction models have often been constrained by aggregated data which may yield inaccurate assessments of the safety performance of a rail network by, for example, failing to consider segment-level characteristics.

This study focused on the development of segment-level derailment prediction models for Canadas rail network using negative binomial and logistic regression modelling methods. The study used a network screening process to identify key segments of derailment concerns. A thorough quantitative review of the models results and performance was conducted to understand the predictive capabilities and applications of the models in derailment prediction. The analytical approach and findings in this study have strong implications for advancing research on rail safety in North America.