2022/03/14 by Jianfeng Wang, Wang, Jianfeng, Roberto Mantas Nakhai +3
Engineering · Mathematics · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Railway Engineering and Dynamics #Railway Systems and Energy Efficiency #Transportation Planning and Optimization #stat.AP
paper · pdf · doi:10.48550/arxiv.2203.06956
arxiv created 2022/03/14 · openalex publication_date 2022/03/14 · arxiv updated 2022/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study investigated the climate effect under consecutive winters on the arrival delay of high-speed passenger trains in northern Sweden. Novel statistical learning approaches, including inhomogeneous Markov chain model and stratified Cox model, were adopted to account for the time-varying risks of train delays. The inhomogeneous Markov chain modelling for the arrival delays has used several covariates, including weather variables, train operational direction, and findings from the primary delay analysis through stratified Cox model. The results showed that the weather variables, such as temperature, snow depth, ice/snow precipitation, and train operational direction, significantly impact the arrival delay. The performance of the fitted inhomogeneous Markov chain model was evaluated by the walk-forward validation method. The averaged mean absolute errors between the expected rates and the observed rates of the arrival delay over the train line was obtained at the level of 0.088, which implies that approximately 9% of trains may be misclassified as having arrival delays by the fitted model at a measuring point on the train line.