A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hinder...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7996 |
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| author | Jin Liu Lei Chen Zhongbei Tian Ning Zhao Clive Roberts |
| author_facet | Jin Liu Lei Chen Zhongbei Tian Ning Zhao Clive Roberts |
| author_sort | Jin Liu |
| collection | DOAJ |
| description | Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. |
| format | Article |
| id | doaj-art-3012c19b8ebe4a8fbd53c30fb18b4904 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3012c19b8ebe4a8fbd53c30fb18b49042025-08-20T02:45:53ZengMDPI AGApplied Sciences2076-34172025-07-011514799610.3390/app15147996A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway NetworksJin Liu0Lei Chen1Zhongbei Tian2Ning Zhao3Clive Roberts4Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UKBirmingham Centre for Railway Research and Education, University of Birmingham, Birmingham B15 2TT, UKBirmingham Centre for Railway Research and Education, University of Birmingham, Birmingham B15 2TT, UKBirmingham Centre for Railway Research and Education, University of Birmingham, Birmingham B15 2TT, UKBirmingham Centre for Railway Research and Education, University of Birmingham, Birmingham B15 2TT, UKReal-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity.https://www.mdpi.com/2076-3417/15/14/7996large-scale optimizationmulti-agent system (MAS)Condorcet votingtrain rescheduling |
| spellingShingle | Jin Liu Lei Chen Zhongbei Tian Ning Zhao Clive Roberts A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks Applied Sciences large-scale optimization multi-agent system (MAS) Condorcet voting train rescheduling |
| title | A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks |
| title_full | A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks |
| title_fullStr | A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks |
| title_full_unstemmed | A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks |
| title_short | A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks |
| title_sort | novel multi agent based approach for train rescheduling in large scale railway networks |
| topic | large-scale optimization multi-agent system (MAS) Condorcet voting train rescheduling |
| url | https://www.mdpi.com/2076-3417/15/14/7996 |
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