A Bayesian network model to predict the effects of interruptions on train operations. Huang, P., Lessan, J., Wen, C., Peng, Q., Fu, L., Li, L., & Xu, X. Transportation Research Part C: Emerging Technologies, 114:338–358, May, 2020. ![A Bayesian network model to predict the effects of interruptions on train operations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{huang_bayesian_2020,
title = {A {Bayesian} network model to predict the effects of interruptions on train operations},
volume = {114},
issn = {0968-090X},
url = {https://www.sciencedirect.com/science/article/pii/S0968090X19311118},
doi = {10.1016/j.trc.2020.02.021},
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.},
language = {en},
urldate = {2021-11-27},
journal = {Transportation Research Part C: Emerging Technologies},
author = {Huang, Ping and Lessan, Javad and Wen, Chao and Peng, Qiyuan and Fu, Liping and Li, Li and Xu, Xinyue},
month = may,
year = {2020},
keywords = {Bayesian networks, Disturbances and disruptions, Real-time prediction, Train operation},
pages = {338--358},
}
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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.","language":"en","urldate":"2021-11-27","journal":"Transportation Research Part C: Emerging Technologies","author":[{"propositions":[],"lastnames":["Huang"],"firstnames":["Ping"],"suffixes":[]},{"propositions":[],"lastnames":["Lessan"],"firstnames":["Javad"],"suffixes":[]},{"propositions":[],"lastnames":["Wen"],"firstnames":["Chao"],"suffixes":[]},{"propositions":[],"lastnames":["Peng"],"firstnames":["Qiyuan"],"suffixes":[]},{"propositions":[],"lastnames":["Fu"],"firstnames":["Liping"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Li"],"suffixes":[]},{"propositions":[],"lastnames":["Xu"],"firstnames":["Xinyue"],"suffixes":[]}],"month":"May","year":"2020","keywords":"Bayesian networks, Disturbances and disruptions, Real-time prediction, Train operation","pages":"338–358","bibtex":"@article{huang_bayesian_2020,\n\ttitle = {A {Bayesian} network model to predict the effects of interruptions on train operations},\n\tvolume = {114},\n\tissn = {0968-090X},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0968090X19311118},\n\tdoi = {10.1016/j.trc.2020.02.021},\n\tabstract = {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. 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