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International Journal of Intelligent Transportation Systems Research
Erschienen in: International Journal of Intelligent Transportation Systems Research 1/2019

22.05.2018

A Modified Genetic Algorithm for Multi-Objective Optimization on Running Curve of Automatic Train Operation System Using Penalty Function Method

verfasst von: Yanchu Liang, Hao Liu, Cunyuan Qian, Guanlei Wang

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 1/2019

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Abstract

The running curve optimization of Automatic Train Operation 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 Automatic Train Operation system, we adopted the multi-objective optimization strategy of genetic algorithm to optimize from five aspects: speeding (safety), parking accuracy, punctuality, energy consumption and comfort. In order to increase the convergence speed of genetic algorithm to the optimal solutions, we propose a modified genetic algorithm, which the penalty function method is added into the fitness objective function. The modified genetic algorithm optimization program is written by M language in MATLAB, and combined with a graphical user interface tool to design the optimization system. 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 Automatic Train Operation system.
Vorheriger Artikel Determinants of Travel Choice Behaviour with Travel-Time Variability in the Presence of Real-Time Information

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Metadaten
Titel
A Modified Genetic Algorithm for Multi-Objective Optimization on Running Curve of Automatic Train Operation System Using Penalty Function Method
verfasst von
Yanchu Liang
Hao Liu
Cunyuan Qian
Guanlei Wang
Publikationsdatum
22.05.2018
Verlag
Springer US
Erschienen in
International Journal of Intelligent Transportation Systems Research / Ausgabe 1/2019
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-018-0158-6

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