A hybrid Bayesian network model for predicting delays in train operations
| dc.contributor.author | Lessan, Javad | |
| dc.contributor.author | Fu, Liping | |
| dc.contributor.author | Wen, Chao | |
| dc.date.accessioned | 2020-02-05T19:17:38Z | |
| dc.date.available | 2020-02-05T19:17:38Z | |
| dc.date.issued | 2019-01 | |
| dc.description | The final publication is available at Elsevier via https://doi.org/10.1016/j.jedc.2018.11.005. © 2018 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.description.abstract | We 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.sponsorship | This 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.identifier.uri | https://doi.org/10.1016/j.cie.2018.03.017 | |
| dc.identifier.uri | http://hdl.handle.net/10012/15617 | |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | high-speed rail | en |
| dc.subject | train operation | en |
| dc.subject | punctuality | en |
| dc.subject | Bayesian networks | en |
| dc.subject | delay prediction | en |
| dc.subject | performance evaluation | en |
| dc.title | A hybrid Bayesian network model for predicting delays in train operations | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | Lessan, J., Fu, L., Wen, C., A Hybrid Bayesian Network Model for Predicting Delays in Train Operations, Computers & Industrial Engineering (2018), doi: https://doi.org/10.1016/j.cie.2018.03.017 | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Civil and Environmental Engineering | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Faculty | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |
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