High-speed train connection optimization for large passenger transport hub based on transfer orientation
QIAO Jun1, MENG Xuelei1, WANG Dongxian1, TANG Lin2
1. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China;
2. Lanzhou Railway Station, China Railway Lanzhou Bureau Group Company Limited, Lanzhou Gansu 730070, China
In view of the optimization of high-speed train connection in passenger transport hub under the condition of high-speed railway network, the concept of transfer satisfaction of medium and long distance passenger flow was proposed by analyzing the passenger transfer process in hub, and a high-speed train connection optimization model for large passenger transport hub based on transfer orientation was proposed with the average transfer satisfaction and the arrival and departure equilibrium of trains at hub stations as the optimization objective and with the constraint conditions of reasonable originating time of large stations, reasonable terminating time, station operation interval time, passenger transfer time and station arrival and departure line capacity. A genetic algorithm with improved chromosome coding mode and selection strategy was designed to solve the example. Compared with the basic genetic algorithm and the basic simulated annealing algorithm, the improved genetic algorithm increases the average transfer satisfaction in the objective function by 5.10% and 2.93% respectively, and raises the equilibrium of arrival and departure of trains at hub stations by 0.27% and 2.31% respectively. The results of the example verify the effectiveness and stability of the improved genetic algorithm, which indicates that the proposed method can effectively optimize the quality of the high-speed train connection in large passenger transport hub.
乔俊, 孟学雷, 王东先, 汤霖. 基于换乘导向的大型客运枢纽高铁列车接续优化[J]. 计算机应用, 2019, 39(9): 2757-2764.
QIAO Jun, MENG Xuelei, WANG Dongxian, TANG Lin. High-speed train connection optimization for large passenger transport hub based on transfer orientation. Journal of Computer Applications, 2019, 39(9): 2757-2764.
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2019, Vol. 39 
