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Energies 2017, 10(4), 436; doi:10.3390/en10040436

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
Ministry of Education (MOE) Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
Authors to whom correspondence should be addressed.
Academic Editor: William Holderbaum
Received: 6 January 2017 / Revised: 19 February 2017 / Accepted: 15 March 2017 / Published: 29 March 2017
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How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach. View Full-Text
Keywords: metro; energy-saving; timetable; fixed running time; dwelling time metro; energy-saving; timetable; fixed running time; dwelling time

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Feng, J.; Li, X.; Liu, H.; Gao, X.; Mao, B. Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands. Energies 2017, 10, 436.

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