Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections
The operation of heavy-haul trains requires frequent stopping, resulting in low traffic efficiency, due to their long formations and high loads, as well as dynamic signaling changes in automatic block sections. Therefore, both scheduled traffic and dynamic stopping need to be taken into account in t...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2024-08-01
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| Series: | Kongzhi Yu Xinxi Jishu |
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| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.04.002 |
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| _version_ | 1849224638639374336 |
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| author | ZHANG Zhengfang XIONG Jiayuan SHU Quanlin LUO Yuan JIANG Jie |
| author_facet | ZHANG Zhengfang XIONG Jiayuan SHU Quanlin LUO Yuan JIANG Jie |
| author_sort | ZHANG Zhengfang |
| collection | DOAJ |
| description | The operation of heavy-haul trains requires frequent stopping, resulting in low traffic efficiency, due to their long formations and high loads, as well as dynamic signaling changes in automatic block sections. Therefore, both scheduled traffic and dynamic stopping need to be taken into account in the decision programming of automatic operation systems for the control of heavy-haul trains in automatic block sections. This paper proposes a double-layer collaborative decision-making model for the automatic operation of heavy-haul trains, based on an analysis of their dynamics characteristics and the characteristics of signaling changes under automatic blocking, to simultaneously ensure traffic efficiency and smooth stopping. This two-layer model is interconnected through coupling points in operation. The upper-layer model takes schedules based on train graphs as constraints to optimize control curves, aiming for energy saving and stable operation, while calculating coupling state intervals under these desired conditions. The lower-layer model incorporates the most unfavorable signaling (stopping) constraints and uses optimal coupling states from the upper model as its starting point for the optimization of control curves, with the goal of ensuring safe and stable stopping. In the automatic operation experiments conducted on the Suozhou-Huanghua East Line, the proposed model achieved zero takeovers throughout the entire journey. Experimental data showed that its effectiveness in improving both the safety and stability of train operation, while ensuring operational efficiency. |
| format | Article |
| id | doaj-art-47415df606bc4caf88c9ae744b9ef7c6 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-47415df606bc4caf88c9ae744b9ef7c62025-08-25T06:57:11ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272024-08-01111867489066Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block SectionsZHANG ZhengfangXIONG JiayuanSHU QuanlinLUO YuanJIANG JieThe operation of heavy-haul trains requires frequent stopping, resulting in low traffic efficiency, due to their long formations and high loads, as well as dynamic signaling changes in automatic block sections. Therefore, both scheduled traffic and dynamic stopping need to be taken into account in the decision programming of automatic operation systems for the control of heavy-haul trains in automatic block sections. This paper proposes a double-layer collaborative decision-making model for the automatic operation of heavy-haul trains, based on an analysis of their dynamics characteristics and the characteristics of signaling changes under automatic blocking, to simultaneously ensure traffic efficiency and smooth stopping. This two-layer model is interconnected through coupling points in operation. The upper-layer model takes schedules based on train graphs as constraints to optimize control curves, aiming for energy saving and stable operation, while calculating coupling state intervals under these desired conditions. The lower-layer model incorporates the most unfavorable signaling (stopping) constraints and uses optimal coupling states from the upper model as its starting point for the optimization of control curves, with the goal of ensuring safe and stable stopping. In the automatic operation experiments conducted on the Suozhou-Huanghua East Line, the proposed model achieved zero takeovers throughout the entire journey. Experimental data showed that its effectiveness in improving both the safety and stability of train operation, while ensuring operational efficiency.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.04.002heavy-haul traindouble-layer optimization modeldynamic programmingautomatic operationautomatic block systemMonte Carlo sampling |
| spellingShingle | ZHANG Zhengfang XIONG Jiayuan SHU Quanlin LUO Yuan JIANG Jie Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections Kongzhi Yu Xinxi Jishu heavy-haul train double-layer optimization model dynamic programming automatic operation automatic block system Monte Carlo sampling |
| title | Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections |
| title_full | Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections |
| title_fullStr | Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections |
| title_full_unstemmed | Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections |
| title_short | Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections |
| title_sort | research on decision making for automatic operation of heavy haul trains in automatic block sections |
| topic | heavy-haul train double-layer optimization model dynamic programming automatic operation automatic block system Monte Carlo sampling |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.04.002 |
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