Dynamic coast control of train movement with genetic algorithm

Wong, K. K. & Ho, T. K. (2004) Dynamic coast control of train movement with genetic algorithm. International Journal of Systems Science, 35(13-14), pp. 835-846.

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The railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. Train service usually varies with the population activities throughout a day and train coordination and service regulation are then expected to meet the daily passengers' demand. Dwell time control at stations and fixed coasting point in an inter-station run are the current practices to regulate train service in most metro railway systems. However, a flexible and efficient train control and operation is not always possible. To minimize energy consumption of train operation and make certain compromises on the train schedule, coast control is an economical approach to balance run-time and energy consumption in railway operation if time is not an important issue, particularly at off-peak hours. The capability to identify the starting point for coasting according to the current traffic conditions provides the necessary flexibility for train operation. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigates the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. Further, a hierarchical genetic algorithm (HGA) is introduced here to identify the number of coasting points required according to the traffic conditions, and Minimum-Allele-Reserve-Keeper (MARK) is adopted as a genetic operator to achieve fitter solutions.

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50 citations in Scopus
45 citations in Web of Science®
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ID Code: 38203
Item Type: Journal Article
Refereed: Yes
Keywords: Raiwlay operation, Coasting control, Train movement, Genetic algorithms
DOI: 10.1080/00207720412331203633
ISSN: 0020-7721
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) > Operations Research (010206)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Expert Systems (080105)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Transport Engineering (090507)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2004 Taylor & Francis Ltd
Copyright Statement: This is an electronic version of an article published in the International Journal of Systems Science, which is available online at informaworldTM
Deposited On: 26 Oct 2010 23:58
Last Modified: 21 Jun 2017 14:44

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