Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning
Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/3072495 |
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| _version_ | 1832564752543383552 |
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| author | Wei Li Kai Xian Jiateng Yin Dewang Chen |
| author_facet | Wei Li Kai Xian Jiateng Yin Dewang Chen |
| author_sort | Wei Li |
| collection | DOAJ |
| description | Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the ±30cm, which meet the requirement of urban rail transit. |
| format | Article |
| id | doaj-art-605aa8fe60ef4926a73c34ceadac1c2b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-605aa8fe60ef4926a73c34ceadac1c2b2025-02-03T01:10:16ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/30724953072495Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement LearningWei Li0Kai Xian1Jiateng Yin2Dewang Chen3School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, ChinaBeijing Transport Institute, No. 9, LiuLiQiao South Lane, Fengtai District, Beijing, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, ChinaCollege of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, ChinaTrain station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the ±30cm, which meet the requirement of urban rail transit.http://dx.doi.org/10.1155/2019/3072495 |
| spellingShingle | Wei Li Kai Xian Jiateng Yin Dewang Chen Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning Journal of Advanced Transportation |
| title | Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning |
| title_full | Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning |
| title_fullStr | Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning |
| title_full_unstemmed | Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning |
| title_short | Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning |
| title_sort | developing train station parking algorithms new frameworks based on fuzzy reinforcement learning |
| url | http://dx.doi.org/10.1155/2019/3072495 |
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