journal article·2022-08-08Open access

Optimization of Train Operation Planning with Full-Length and Short-Turn Routes of Virtual Coupling Trains

Xu Zhou, Fang Lu, Liyu Wang

Applied Sciences· Volume 12· Issue 15

Abstract

Virtual coupling uses wireless communication instead of mechanical coupling to ensure that trains are easily reconnected or disconnected. This technology can shorten the interval time between trains, give full play to the carrying capacity of lines, and improve the service level of urban rail transit. This paper optimizes the train operation plan with full-length and short-turn routes of virtual coupling trains by establishing a two-level optimization model. The upper model is used to minimize passenger travel time and enterprise operation cost, and the lower model to optimize the equilibrium of train load rate on short-turn routes. Meanwhile, a method based on the genetic algorithm is designed to solve the model. A case study of the Metro Line M has been carried out. The results can verify the efficiency and feasibility of the proposed method. The full-length and short-turn routes of virtual coupling trains can effectively reduce passenger travel time, enterprise operating cost and the number of vehicles, and improve the average load factor of the trains. Finally, sensitivity analyses are performed using three parameters which include departure frequency of the full-length train and short-turn train, starting and terminal station of short-turn route, and number of marshalled vehicles of the full-length train and short-turn train.
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Research context

OpenAlex topics: Railway Systems and Energy Efficiency · Transportation Planning and Optimization · Railway Engineering and Dynamics

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China

Traffic Control Technology Co., Ltd., Beijing 100070, China

Funding

Beijing Natural Science Foundation

References (36)

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