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dc.contributor.authorLessan, Javad
dc.contributor.authorFu, Liping
dc.contributor.authorWen, Chao 19:17:38 (GMT) 19:17:38 (GMT)
dc.descriptionThe final publication is available at Elsevier via © 2018 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.description.abstractWe present a Bayesian network-(BN) based train delay prediction model to tackle the complexity and dependency nature of train operations. Three different BN schemes, namely, heuristic hill-climbing, primitive linear and hybrid structure, are investigated using real-world train operation data from a high-speed railway line. We first use historical data to rationalize the dependency graph of the developed structures. Each BN structure is then trained with the gold standard k-fold cross validation approach to avoid over-fitting and evaluate its performance against the others. Overall, the validation results indicate that a BN-based model can be an efficient tool for capturing superposition and interaction effects of train delays. However, a well-designed hybrid BN structure, developed based on domain knowledge and judgments of expertise and local authorities, can outperform the other models. We present a performance comparison of the predictions obtained from the hybrid BN structure against the real-world benchmark data. The results show that the proposed model on overage can achieve over 80% accuracy in predictions within a 60-min horizon, yielding low prediction errors regarding mean absolute error (MAE), mean error (ME) and root mean square error (RMSE) measures.en
dc.description.sponsorshipThis work was supported by the National Nature Science Foundation of China [Grant No. 61503311], National Key R&D Plan of China [Grant No. 2017YFB1200701] and NSERC (National Sciences and Engineering Research Council of Canada). We acknowledge the support of the Railways Technology Development Plan of China Railway Corporation [Grant No. 2016X008-J]. Parts of the work were supported by State Key Lab of Railway Control and Safety Open Topics Fund [Grant No. RCS2017K008].en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjecthigh-speed railen
dc.subjecttrain operationen
dc.subjectBayesian networksen
dc.subjectdelay predictionen
dc.subjectperformance evaluationen
dc.titleA hybrid Bayesian network model for predicting delays in train operationsen
dcterms.bibliographicCitationLessan, J., Fu, L., Wen, C., A Hybrid Bayesian Network Model for Predicting Delays in Train Operations, Computers & Industrial Engineering (2018), doi:
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Civil and Environmental Engineeringen

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