Train performance analysis using heterogeneous statistical models

TL;DR

This paper introduces heterogeneous statistical models to analyze train performance under harsh winter conditions, accounting for time-varying delay risks and identifying key weather factors affecting train delays.

Contribution

It presents novel stratified Cox and heterogeneous Markov chain models for analyzing train delays considering weather impacts and delay dynamics.

Findings

Weather variables significantly affect train delays

Heterogeneous models effectively capture delay risks

Temperature, humidity, snow depth impact train performance

Abstract

This study investigated the effect of harsh winter climate on the performance of high speed passenger trains in northern Sweden. Novel approaches based on heterogeneous statistical models were introduced to analyse the train performance in order to take the time-varying risks of train delays into consideration. Specifically, stratified Cox model and heterogeneous Markov chain model were used for modelling primary delays and arrival delays, respectively. Our results showed that the weather variables including temperature, humidity, snow depth, and ice/snow precipitation have significant impact on the train performance.