  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.
