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This study investigated the climate effect under consecutive winters on thearrival delay of high-speed passenger trains in northern Sweden. Novelstatistical learning approaches, including inhomogeneous Markov chain model andstratified Cox model, were adopted to account for the time-varying risks oftrain delays. The inhomogeneous Markov chain modelling for the arrival delayshas used several covariates, including weather variables, train operationaldirection, and findings from the primary delay analysis through stratified Coxmodel. The results showed that the weather variables, such as temperature, snowdepth, ice/snow precipitation, and train operational direction, significantlyimpact the arrival delay. The performance of the fitted inhomogeneous Markovchain model was evaluated by the walk-forward validation method. The averagedmean absolute errors between the expected rates and the observed rates of thearrival delay over the train line was obtained at the level of 0.088, whichimplies that approximately 9% of trains may be misclassified as having arrivaldelays by the fitted model at a measuring point on the train line.

Statistical learning for train delays and influence of winter climate and atmospheric icing