Small-data Driven Modeling and Simulation of High-speed Train Running Time Under Limited Speeds

doi:10.16182/j.issn1004731x.joss.20-0329

Abstract

Abstract: In order to provide data support and evaluate the feasibility of high-speed train group scheduling optimization algorithm, a method of combining the mechanism model with the small-data drive is proposed. The train segment fitting model under speed limit is constructed and parameterized to reduce the number of parameters to be identified: In order to avoid the improper fitting, a parameter fitting algorithm based on the particle swarm optimization and the least square is proposed. “Location-Time-Speed” model for temporary speed limits together are proposed. The model is demonstrated on the simulation platform, and the train running time is simulated accurately and quickly under various speed limit conditions, and the effectiveness and availability of the model and method are verified.

Key words: train running time, temporary speed limits, small-data driven, parameter fitting, simulation platform

CLC Number:

TP391.9

Xu Peng, Feng Guoqi, Dai Xuewu, Cui Dongliang, Wei Qilong, Li Baoxu, Li Jianming. Small-data Driven Modeling and Simulation of High-speed Train Running Time Under Limited Speeds[J]. Journal of System Simulation, 2021, 33(8): 1892-1904.

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