Stochastic modelling of train delays and delay propagation in stations

Abstract

A trade-off exists between efficiently utilizing the capacity of railway networks and improving the reliability and punctuality of train operations. This dissertation presents a new analytical probability model based on blocking time theory which estimates the knock-on delays of trains caused by route conflicts and late transfer connections in stations. The model estimates the propagation of train delays with a higher accuracy than existing analytical models by taking into account the interdependences of the arrival and departure times of different train lines and the dependences of the dwell times of trains on arrival delays. A detailed statistical analysis of real-world traffic data reveals that the variations of train events and process times can be well approximated by either the lognormal distribution or the Weibull distribution. Given the mean and standard deviation of input delays at the boundary of a station and those of primary delays within the area, the knock-on and exit delay distributions are estimated by means of the stochastic models. Consequently, the maximal traffic capacity utilization of complex stations and interlocking areas can be estimated according to a desired level of train punctuality. The research results support railway infrastructure managers, timetable designers, and train operators in optimizing the network capacity utilization and train scheduling.