Identifying the rail operating features associated to intermodal freight rail operation delays

Abstract

Intermodal freight rail operations represent a complex stochastic system that is impacted by disruptions and disturbances from diverse causes like extreme weather events, unplanned upstream network delays, equipment failures, labor actions, and intra-railyard inefficiency, which in turn generate delays in travel times. Understanding and predicting the delays caused by the occurrence of these disruptions and disturbances holds the potential to limit their system-wide schedule impact through early-warning prompting mitigating actions.This paper presents the training of a suite of supervised machine learning models using classification algorithms to predict the delay times caused by the occurrence of disruptions and disturbances in intermodal freight rail operations, and the most suitable model in terms of the evaluation metrics (e.g., AUC, recall, and F1-score) was used to explore the major predictors of the delays caused by disturbances and disruptions (using the Morris method). The supporting dataset includes intermodal freight rail operations with origin the central station of the freight rail network of CFL, the National Railway Company of Luxembourg, in the intermodal hub of Bettembourg, connecting several EU countries terminals forming a pan-European network.Results reveal that the CatBoost implementation of the gradient boosting machine model outperforms other ML models in terms of the selected metrics. Additionally, results suggest that the train weight, train length, number of TEU, weight per wagon, distance between stations, and the month of operation are key features to predict the delays caused by the occurrence of disruptions and disturbances in the freight operations in the studied rail network. The outcome of the study suggests that longer and more heavily loaded trains are related to the occurrence of trip delays, and this insight can be used to optimize the freight operations of the National Railway Company of Luxembourg.