Optimization of train schedule for urban rail considering operation energy-saving and train circulation planning

被引:0
|
作者
Zhou W. [1 ]
Huang Y. [1 ]
Deng L. [1 ]
机构
[1] School of Traffic and Transportation Engineering, Central South University, Changsha
关键词
energy-efficient train scheduling; particle swarm algorithm; train circulation planning; train tracking strategy; urban rail transit;
D O I
10.19713/j.cnki.43-1423/u.T20220474
中图分类号
学科分类号
摘要
Energy consumption is one of the main costs of urban rail operation. With the increase in energy price, urban rail operation density, and mileage, the energy consumption cost accounts for an increasing share of urban rail operation costs. Optimizing train schedules and realizing energy saving is of great practical significance to reduce the operation cost of urban rail transit. The order to avoid blindly pursuing the energy-saving effect of the schedule leads to the increase in the cost of the use of the rolling stock meeting the train circulation plan, which leads to the increase in the total running cost of the train. This paper proposed an urban rail schedule optimization model considering energy saving and a train circulation plan based on the initial train schedule and candidate traction strategies. First, the optimization objective was set to minimize the total cost of train traction energy consumption and rolling stock operation time, and constraints such as the adjustment of trains’ arrival and departure times, all trains’ travel times, the rolling stock connection time, and the safety headway were considered. An urban rail schedule optimization model considering energy saving and a train circulation plan was constructed to realize the coordinated optimization of the trains’ traction strategies in each rail section, the trains’ arrival and departure times at each station, and the connection plan of the rolling stocks. Second, an efficient particle swarm optimization (PSO) algorithm was designed to solve the model based on constructing the generation strategy of the train circulation plan and the repair strategy of an unfeasible solution. Finally, several numerical experiments based on Guangzhou Metro Line 9 of China illustrate that the collaborative optimization method can reduce the total operation cost of trains by 3.81% as compared to the initial train schedule. © 2023, Central South University Press. All rights reserved.
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页码:473 / 482
页数:9
相关论文
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