A Bayesian network model to predict the effects of interruptions on train operations. (May 2020)

• Record Type: Journal Article
• Authors: Huang, Ping; Lessan, Javad; Wen, Chao; Peng, Qiyuan; Fu, Liping; Li, Li; Xu, Xinyue
• Is Part Of: Transportation research. Volume 114(2020)
• Page Start: 338
• Page End: 358
• Publication Date: 2020-05
• Subjects: Train operation -- Disturbances and disruptions -- Real-time prediction -- Bayesian networks; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.02.021
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Three factors are ascertained to measure the effects of disruptions. Real-time prediction requirements are particularly considered in the model. The model shows high accuracy in predicting the effects of disruptions. The model shows strong generalizability on two different high-speed railway lines. Abstract: 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.