Energy Efficient Metro Train Running Time Rescheduling Model for Fully Automatic Operation Lines
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 7
Abstract
Reducing energy consumption without degrading the normal operation of metro trains and service quality has received increasing attention. Besides, fully automatic operation (FAO), for which no drivers and crew attendants are needed and all functions are controlled automatically, has been applied as a new generation train operation integrated control technology to achieve better performance. In this paper, a two-level energy-efficient optimization approach is proposed based on the characteristics of the FAO system. First, we formulate a single train trajectory optimization model and develop a genetic algorithm to calculate the optimal speed curves with variable running time. The Pareto frontier that represents the relationship between energy consumption and running time can be obtained. Second, we propose a sensitivity analysis method to distribute the total running time among different station segments based on the Pareto solutions in Level 1. Furthermore, the global optimal solution can be obtained. Finally, a case study of the Nanning Rail Transit Line 5 demonstrates that an optimal distribution of running time leads to extra energy savings compared to the original timetable.
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Data Availability Statement
Some or all of the data, models, and code that support the finding of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research was supported by the National Natural Science Foundation of China (Grant No. 517650060, Natural Science Foundation of Guangxi Province of China (Grant No. 2017GXNSFDA198012), Nanning Excellent Young Scientist Program (Grant No. RC20190204), Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund (Grant No. 19-050-44-S015), and the Innovation Project of Guangxi Graduate Education (Grant No. YCSW2020017). Major Science and Technology Project of Guangxi Province of China (Grant No. Guike AA20302010).
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Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 7 • July 2021
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© 2021 American Society of Civil Engineers.
History
Received: Oct 9, 2020
Accepted: Feb 23, 2021
Published online: May 10, 2021
Published in print: Jul 1, 2021
Discussion open until: Oct 10, 2021
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