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- Publisher Website: 10.1093/tse/tdy001
- Scopus: eid_2-s2.0-85086242034
- WOS: WOS:000646083700006
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Article: Train delay analysis and prediction based on big data fusion
Title | Train delay analysis and prediction based on big data fusion |
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Authors | |
Keywords | data fusion machine learning railway operation train delay |
Issue Date | 2019 |
Citation | Transportation Safety and Environment, 2019, v. 1, n. 1, p. 79-88 How to Cite? |
Abstract | Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport, train delays often occur. For this study, a three-month dataset of weather, train delay and train schedule records was collected and analysed in order to understand the patterns of train delays and to predict train delay time. We found that in severe weather train delays are determined mainly by the type of bad weather, while in ordinary weather the delays are determined mainly by the historical delay time and delay frequency of trains. Identifying the factors closely correlated with train delays, we developed a machine-learning model to predict the delay time of each train at each station. The prediction model is useful not only for passengers wishing to plan their journeys more reliably, but also for railway operators developing more efficient train schedules and more reasonable pricing plans. |
Persistent Identifier | http://hdl.handle.net/10722/330635 |
ISSN | 2023 SCImago Journal Rankings: 0.480 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, Pu | - |
dc.contributor.author | Zhang, Qing Peng | - |
dc.date.accessioned | 2023-09-05T12:12:32Z | - |
dc.date.available | 2023-09-05T12:12:32Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Transportation Safety and Environment, 2019, v. 1, n. 1, p. 79-88 | - |
dc.identifier.issn | 2631-6765 | - |
dc.identifier.uri | http://hdl.handle.net/10722/330635 | - |
dc.description.abstract | Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport, train delays often occur. For this study, a three-month dataset of weather, train delay and train schedule records was collected and analysed in order to understand the patterns of train delays and to predict train delay time. We found that in severe weather train delays are determined mainly by the type of bad weather, while in ordinary weather the delays are determined mainly by the historical delay time and delay frequency of trains. Identifying the factors closely correlated with train delays, we developed a machine-learning model to predict the delay time of each train at each station. The prediction model is useful not only for passengers wishing to plan their journeys more reliably, but also for railway operators developing more efficient train schedules and more reasonable pricing plans. | - |
dc.language | eng | - |
dc.relation.ispartof | Transportation Safety and Environment | - |
dc.subject | data fusion | - |
dc.subject | machine learning | - |
dc.subject | railway operation | - |
dc.subject | train delay | - |
dc.title | Train delay analysis and prediction based on big data fusion | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1093/tse/tdy001 | - |
dc.identifier.scopus | eid_2-s2.0-85086242034 | - |
dc.identifier.volume | 1 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 79 | - |
dc.identifier.epage | 88 | - |
dc.identifier.eissn | 2631-4428 | - |
dc.identifier.isi | WOS:000646083700006 | - |