Statistical analysis of departure deviations from shunting yards: Case study from Swedish railways

Abstract

Departure deviations from shunting yards impact the reliability of rail freight services and the punctuality of a railway network. Therefore, the statistical analysis of these deviations are necessary for improving the operation of trains in mixed-traffic networks. In our paper, we conduct a detailed statistical analysis of departure deviations considering individual shunting yards characteristics. We use a large freight train delay dataset comprising 250,000 departures over seven years for the two largest shunting yards in Sweden, comparable to other medium-sized shunting yards in Europe. To find the probability distribution of departure deviations, we compare four distribution functions including the exponential, the log-normal, the gamma, and the Weibull according to the maximum likelihood estimates and results of the Anderson-Darling goodness of fit test. In our experiments, we show that the log-normal distribution fits best for delayed departures across both shunting yards, and for early departures at one of them, whereas the gamma distribution fits best for early departures at the other yard. For the temporal delay distribution, we find that fluctuations in the network usage impact the percentage of delayed departures across hours and weekdays, but not across months or years. In addition, we find that freight trains are mostly delayed in the winter. In the case of hourly delayed departures, we demonstrate that a shunting yard involved with domestic traffic showed a negative correlation between delayed departures and the network usage, whereas an international shunting yard did not, which indicates individuality in shunting yard operations impact shunting yard-network interactions. Our findings mainly contribute to better understanding of departure deviations from shunting yards, thus enhancing the operations and capacity utilization of shunting yards. Moreover, delay distributions can be beneficial in handling delays in traffic management models as well as enhancing the outputs of freight train simulation models.