Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L, N, and T, respectively.

基于贝叶斯网络（BN）范式，我们提出了一种混合模型来预测列车运行过程中干扰和干扰的三个主要后果，即主要延迟（L），受影响列车的数量（N）和总数延迟时间（Ť）。为了获得有效的BN结构，我们首先要在给定的领域知识和专业知识的基础上，分析每个站点以及相邻站点之间相关因素的依赖性。然后，我们提出了四个候选BN结构，这些结构整合了专家知识，从现实世界数据中学到的相互依赖性以及实时预测和操作需求。接下来，我们使用来自武汉-广州（WG）和厦门-深圳（XS）高速铁路（HSR）线的运营数据，基于5倍交叉验证方法来训练候选结构。提名了表现最佳的结构来预测两条高铁线路中断和干扰的后果。比较结果表明，所提出的模型优于其他三种常用的预测模型，L，N和T分别。