Stochastic multi-objective optimization for dynamic timetable and track allocation at high-speed railway hubs
Train scheduling and track allocation are crucial for minimizing passenger flow conflicts, ensuring safety, and enhancing travel experience at railway hubs. This study presents a collaborative optimization model for railway hub operations, focusing on improving passenger transfer efficiency and mini...
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| Vydáno v: | International Journal of Transportation Science and Technology |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.03.2025
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| ISSN: | 2046-0430 |
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| Abstract | Train scheduling and track allocation are crucial for minimizing passenger flow conflicts, ensuring safety, and enhancing travel experience at railway hubs. This study presents a collaborative optimization model for railway hub operations, focusing on improving passenger transfer efficiency and minimizing arrival-departure track (ADT) utilization costs. A multi-objective hybrid model, referred to as COTADT, is developed to address spatiotemporal and stochastic constraints while balancing these two objectives. To solve this model efficiently, a customized augmented epsilon-constraint algorithm (CAEC) is introduced, utilizing augmented constraints and interval partitioning to generate Pareto-optimal solutions. The approach is validated with real-world data from the Hangzhou Hub, yielding significant improvements, including a 23.149% reduction in passenger transfer costs and a 7.101% reduction in ADT utilization costs. Comparative experiments demonstrate that CAEC outperforms heuristic algorithms in both solution quality and computational efficiency. This research provides a robust, scalable framework for enhancing operational performance and passenger experience at railway hubs. |
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| AbstractList | Train scheduling and track allocation are crucial for minimizing passenger flow conflicts, ensuring safety, and enhancing travel experience at railway hubs. This study presents a collaborative optimization model for railway hub operations, focusing on improving passenger transfer efficiency and minimizing arrival-departure track (ADT) utilization costs. A multi-objective hybrid model, referred to as COTADT, is developed to address spatiotemporal and stochastic constraints while balancing these two objectives. To solve this model efficiently, a customized augmented epsilon-constraint algorithm (CAEC) is introduced, utilizing augmented constraints and interval partitioning to generate Pareto-optimal solutions. The approach is validated with real-world data from the Hangzhou Hub, yielding significant improvements, including a 23.149% reduction in passenger transfer costs and a 7.101% reduction in ADT utilization costs. Comparative experiments demonstrate that CAEC outperforms heuristic algorithms in both solution quality and computational efficiency. This research provides a robust, scalable framework for enhancing operational performance and passenger experience at railway hubs. |
| Author | Huang, Lu Yan, Bing Qu, Siyuan Fan, Xiaodong Zou, Xiaolei Wan, Chen |
| Author_xml | – sequence: 1 givenname: Bing surname: Yan fullname: Yan, Bing organization: Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China – sequence: 2 givenname: Lu surname: Huang fullname: Huang, Lu organization: School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China – sequence: 3 givenname: Chen surname: Wan fullname: Wan, Chen organization: Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China – sequence: 4 givenname: Siyuan surname: Qu fullname: Qu, Siyuan organization: Transportation Department of China Railway Shanghai Group Co., Ltd., Shanghai 200040, China – sequence: 5 givenname: Xiaodong surname: Fan fullname: Fan, Xiaodong organization: Shanghai Metro No.1 Operation Co., Ltd, Shanghai 200003, China – sequence: 6 givenname: Xiaolei surname: Zou fullname: Zou, Xiaolei email: zouxiaolei@tongji.edu.cn organization: Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Tongji University, Shanghai 201804, China |
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| Keywords | Epsilon-constraint algorithm Railway hub Pareto optimality Multi-objective optimization Arrival-departure track allocation |
| Language | English |
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| SubjectTerms | Arrival-departure track allocation Epsilon-constraint algorithm Multi-objective optimization Pareto optimality Railway hub |
| Title | Stochastic multi-objective optimization for dynamic timetable and track allocation at high-speed railway hubs |
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