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2018 | OriginalPaper | Chapter

Research on Running Curve Optimization of Automatic Train Operation System Based on Genetic Algorithm

Authors : Hao Liu, Cunyuan Qian, Zhengmin Ren, Guanlei Wang

Published in: Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017

Publisher: Springer Singapore

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Abstract

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.

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Metadata
Title
Research on Running Curve Optimization of Automatic Train Operation System Based on Genetic Algorithm
Authors
Hao Liu
Cunyuan Qian
Zhengmin Ren
Guanlei Wang
Copyright Year
2018
Publisher
Springer Singapore
Book
Proceedings of the 3rd International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2017

Print ISBN: 978-981-10-7985-6

Electronic ISBN: 978-981-10-7986-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7986-3_91

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