Railroad accident analysis using extreme gradient boosting

Abstract

Railroads are critical to the economic health of a nation. Unfortunately, railroads lose hundreds of millions of dollars from accidents each year. Trends reveal that derailments consistently account for more than 70% of the U.S. railroad industry's average annual accident cost. Hence, knowledge of explanatory factors that distinguish derailments from other accident types can inform more cost-effective and impactful railroad risk management strategies. Five feature scoring methods, including ANOVA and Gini, agreed that the top four explanatory factors in accident type prediction were track class, type of movement authority, excess speed, and territory signalization. Among 11 different types of machine learning algorithms, the extreme gradient boosting method was most effective at predicting the accident type with an area under the receiver operating curve (AUC) metric of 89%. Principle component analysis revealed that relative to other accident types, derailments were more strongly associated with lower track classes, non-signalized territories, and movement authorizations within restricted limits. On average, derailments occurred at 16 kph below the speed limit for the track class whereas other accident types occurred at 32 kph below the speed limit. Railroads can use the integrated data preparation, machine learning, and feature ranking framework presented to gain additional insights for managing risk, based on their unique operating environments.

A novel method on estimating financial consequences of derailment accidents caused by track defections using the Extra Trees method

“…Therefore, estimating and mitigating the consequences of potential accidents would increase railway operational efficiency. The goal of the (Bridgelall & Tolliver, 2021) study was to identify factors associated with the most frequent and expensive types of accidents that are not attributable to human error. Their paper on railway accidents states that nearly 71% of the average annual financial loss of US railways is related to derailment accidents.…”

Tutorial on Support Vector Machines

“…A blind approach to finding the best hyperparameters for a classifier is to conduct several cross-validation cycles and plot the performance trends with different hyperparameter settings (Bridgelall and Tolliver 2021). Of course, doing so also requires selecting the best crossvalidation type, the cross-validation parameters, the performance scores to monitor, and the number cycles to average those scores.…”

The prediction of potential risk path in railway traffic events

“…combined the zero-inflated Poisson regression model, the zero-inflated negative-binomial regression model, the zero-inflated gamma regression model and the zero-inflated log-normal regression model to propose a new approach for evaluating the damages resulting from railway accidents in Korea. Bridgelall and Tolliver [16] analyzed railroad derailment accidents using extreme gradient boosting. An interpretive structural modeling and Bayesian network combining approach was applied by Huang et al [17].…”