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Energy Saving Optimization of Train Operation Timetable Based on a Dueling DQN Algorithm
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Fei LIU1, Fanghui TANG1, Linting LIU1, Wenbin HU2, Jinbing HA2, Cheng QIAN2
Urban Rapid Rail Transit | 2024, 37(2) : 39 - 46
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Urban Rapid Rail Transit | 2024, 37(2): 39-46
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Energy Saving Optimization of Train Operation Timetable Based on a Dueling DQN Algorithm
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Fei LIU1, Fanghui TANG1, Linting LIU1, Wenbin HU2, Jinbing HA2, Cheng QIAN2
Affiliations
  • 1 Operation Management Center Suzhou Rail Transit Group Co. Suzhou Jiangsu 215101
  • 2 Nanjing University of Science and Technology Nanjing 210014
doi: 10.3969/j.issn.1672-6073.2024.02.006
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Subway traction energy consumption can be reduced by optimizing subway timetables. To solve the problem of the impact of passenger flow fluctuations and train delays on the actual energysaving rate, this study proposes a Dueling Deep Q Network (DQN) deep reinforcement learning timetable optimization algorithm combined with a realtime subway power supply current flow calculation model. An interval iterative optimization model based on the spatiotemporal distribution of the dynamic passenger flow was established to suppress the impact of passenger flow variation. The Adaptive Moment Estimation (Adam) and root mean square propagation (RMSProp) methods were applied to predict the Qnetwork and target Qnetwork as well as improve the convergence speed of the model. While minimizing passenger transfer, waiting, and total travel times, this model allows for the seamless switching of energysaving timetables. The test results for Suzhou Line 4 demonstrate the effectiveness of the proposed method. Under the conditions that the arrival time deviation at transfer stations was less than 2 s and the overall operating time of trains remained unchanged, the traction energy saving was 5.27%, and the train kilometer energy consumption decreased by 4.99%.

urban rail transit  /  timetable optimization  /  traction energy saving  /  Dueling DQN  /  dynamic passenger traffic
Fei LIU, Fanghui TANG, Linting LIU, Wenbin HU, Jinbing HA, Cheng QIAN. Energy Saving Optimization of Train Operation Timetable Based on a Dueling DQN Algorithm[J]. Urban Rapid Rail Transit, 2024 , 37 (2) : 39 -46 . DOI: 10.3969/j.issn.1672-6073.2024.02.006
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doi: 10.3969/j.issn.1672-6073.2024.02.006
  • Receive Date：2023-07-04
  • Online Date：2025-07-09
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  • Received：2023-07-04
  • Revised：2024-01-04
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    1 Operation Management Center Suzhou Rail Transit Group Co. Suzhou Jiangsu 215101
    2 Nanjing University of Science and Technology Nanjing 210014
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