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Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network
| 题名： | Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network |
| 正文语种： | eng |
| 作者： | Panukorn Taleongpong;Simon Hu;Zhoutong Jiang;Chao Wu;Sunday Popo-Ola;Ke Han |
| 作者单位： | Center for Transport Studies Department of Civil and Environmental Engineering Imperial College London;School of Civil Engineering Zhejiang University/University of Illinois at Urbana-Champaign Institute (ZJU-UIUC Institute);Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign;School of Public Affairs Zhejiang University;Center for Transport Studies Department of Civil and Environmental Engineering Imperial College London;School of Transportation and Logistics Southwest Jiaotong University |
| 关键词： | gradient boosting;machine learning;railway delay prediction;reactionary delay |
| 摘要： | Abstract Reactionary delays that propagate from a primary source throughout train journeys are an immediate concern for British railway systems. Complex non-linear interactions between various spatiotemporal variables govern the propagation of these delays which can avalanche throughout railway network causing further severe disruptions. This paper introduces several machine learning (ML) techniques alongside data mining processes to create a framework that predicts key performance indicators (KPIs), reactionary arrival delay, reactionary departure delay, dwell time and travel time. The frameworks in this paper provide greater accuracy in predicting KPIs through state-of-the-art ML models compared to existing delay prediction systems. Further discussion on the improvements, applicability and scalability of this framework are also provided in this paper. |
| 出版年： | 2022 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 26 |
| 期： | 1/6 |
| 页码： | 316-334 |
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