Reliability and punctuality are the key evaluation criteria in railway service for both passengers and operators. Delays spanning over spatial and temporal dimensions significantly affect the reliability and punctuality level of train operation. The optimization of capacity utilization and timetable design requires the prediction of the reliability and punctuality level of train operations, which is determined by train delays and delay propagation. To predict the punctuality level of train operations, the distributions of arrival and departure delays must be estimated as realistically as possible by taking into account the complex railway network structure and different types of delays caused by route conflict and connected trips. This paper aims to predict the propagation of delays on the railway network in the Greater Sydney area by developing a conditional Bayesian model. In the model, the propagation satisfies the Markov property if one can predict future delay propagation in the network based solely on its present state just as well as one could knowing the process’s full history, so that it is independent of such historical procedures. Meanwhile, we consider the throughput estimation for the cases of delay caused by interchange line conflicts and train connection in this model. To the best of the authors’ knowledge, this is the first work of data-driven delay propagation modeling that examines both spatial and temporal dimensions under four different scenarios for railway networks. Implementation on real-world railway network operation data shows the feasibility and accuracy of the proposed model compared with traditional probability models.

可靠性和准点性是乘客和运营商对铁路服务的主要评价标准。跨越空间和时间维度的延误显着影响列车运行的可靠性和准点水平。运力利用和时刻表设计的优化需要预测列车运行的可靠性和准点水平，这是由列车延误和延误传播决定的。为了预测列车运行的准点水平，必须考虑到复杂的铁路网络结构以及由路线冲突和连接行程引起的不同类型的延误，尽可能真实地估计到发延误的分布。本文旨在通过开发条件贝叶斯模型来预测大悉尼地区铁路网络延误的传播。在该模型中，如果可以仅根据网络的当前状态预测网络中的未来延迟传播，就像了解过程的完整历史一样，则传播满足马尔可夫性质，因此它独立于这些历史过程。同时，我们在该模型中考虑了由换乘线路冲突和列车连接引起的延迟情况的吞吐量估计。据作者所知，这是数据驱动延迟传播建模的第一项工作，它检查了铁路网络四种不同场景下的空间和时间维度。