A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances
Considering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem. This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8882554 |
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| _version_ | 1832546995477151744 |
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| author | Jinlin Liao Feng Zhang Shiwen Zhang Cheng Gong |
| author_facet | Jinlin Liao Feng Zhang Shiwen Zhang Cheng Gong |
| author_sort | Jinlin Liao |
| collection | DOAJ |
| description | Considering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem. This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning. The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network. A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems. Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem. The experimental results show that in the two-train metro system, the three-train metro system, and the five-train metro system, the MGA-GRU method can save an average of energy by 4.45%, 6.16%, and 7.19%, while the average decision time is only 0.15 s, 0.27 s, and 0.33 s, respectively. |
| format | Article |
| id | doaj-art-21c97f8033584aa1a209491157e7f0d1 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-21c97f8033584aa1a209491157e7f0d12025-02-03T06:46:26ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88825548882554A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random DisturbancesJinlin Liao0Feng Zhang1Shiwen Zhang2Cheng Gong3School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USAConsidering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem. This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning. The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network. A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems. Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem. The experimental results show that in the two-train metro system, the three-train metro system, and the five-train metro system, the MGA-GRU method can save an average of energy by 4.45%, 6.16%, and 7.19%, while the average decision time is only 0.15 s, 0.27 s, and 0.33 s, respectively.http://dx.doi.org/10.1155/2020/8882554 |
| spellingShingle | Jinlin Liao Feng Zhang Shiwen Zhang Cheng Gong A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances Journal of Advanced Transportation |
| title | A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances |
| title_full | A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances |
| title_fullStr | A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances |
| title_full_unstemmed | A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances |
| title_short | A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances |
| title_sort | real time train timetable rescheduling method based on deep learning for metro systems energy optimization under random disturbances |
| url | http://dx.doi.org/10.1155/2020/8882554 |
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