Research on Running Curve Optimization of Automatic Train Operation System Based on Genetic Algorithm
The running curve optimization of automatic train operation (ATO) system usually takes into account running time, energy consumption and passenger comfort. In this paper, in order to provide more comprehensive optimization and accurate reference of running curve for ATO system, we adopted the multi-objective optimization strategy of genetic algorithm (GA) to optimize from five aspects: speeding (safety), parking accuracy, punctuality, energy consumption and comfort. The GA optimization program is written by M language in MATLAB, and combined with a graphical user interface (GUI) tool to design the optimization system of running curve of ATO based on genetic algorithm. Its validity is verified by comparison between the tests based on three different interstation of Shanghai Metro Line 11. The results show that it is effective and practicability to use the designed system to optimize the running curve of ATO system.
KeywordsATO (Automatic train operation) GA (Genetic algorithm) Multi-objective optimization Running curve Urban rail transit
This work is supported by the National “Twelfth Five-Year” Pillar program for Science & Technology – the Interoperability Comprehensive Evaluation Integrative Platform and Demonstration for Urban Rail Transit (No.2015BAG19B02).
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