Developing Markov chain models for train delay evolution in winter climate

The traffic on Swedish railways is increasing and punctuality is of important matter for both passenger and freight trains. The problem of modeling train delay evolution is complex since conflicts between trains can occur and since a delay can have a wide variety of causes. Swedish railways faces in addition harsh winter climate. Studies of railways in Scandinavia have shown that harsh winter climate decreases the punctuality of trains. This thesis work investigates the possibilities of modeling train delay evolution as continuous time Markov processes and which specific modeling choices are preferable. It also further assesses the impact of a harsh winter climate on the delay evolution. The studied segments are Stockholm - Umeå and Luleå - Kiruna. Both over one winter season. It was found that a change in the time schedule, which in a way redefines the delay, allows for a better fit and better prediction capabilities. It reduced the MSE of the prediction by 50 %. As for the weather variables, four variables were included together with their week long moving averages. Low temperatures were found to increase the risk of a delay (Hazard ratio of 1.10) as well as to decrease the chance of recovering from a delay (Hazard ratio of 0.91). No other significant weather impacts were found