A Timetable Optimization Model for the Istanbul, Turkey, Metro Network Considering a Novel Regenerative Braking Energy Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 2
Abstract
Optimization of train operation is crucial in rail transport networks where large amounts of energy are used. The energy-efficient operation of trains is seen to be a good approach to saving operating costs in metro systems. There are two main techniques for efficient train operation. These are decreasing traction energy and increasing the usage of regeneration energy. In order to make maximum use of the regenerative energy of trains, an integrated optimization method that calculates the optimum train speed trajectory and timetable is proposed in this study. The suggested model aimed to increase the overlapping time of braking and accelerating train groups by improving the timetable. In addition, it is intended that two trains in the same electric zone do not accelerate or brake simultaneously so that the catenary voltage does not rise and decrease too much. The Istanbul M3 subway system with its real operating data is modeled and simulated. The genetic algorithm and simulated annealing algorithm were employed in the optimization process. The outputs of the study show that the consumption of traction energy can be substantially decreased by employing the best train speed profile and timetable.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
The authors of the paper acknowledge the collaboration of Metro Istanbul in providing data and allowing for test facilities under operation.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 2 • February 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 26, 2023
Accepted: Sep 21, 2023
Published online: Dec 12, 2023
Published in print: Feb 1, 2024
Discussion open until: May 12, 2024
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