Abstract

High-speed railways have been developing quickly in recent years and have become a main travel mode between cities in many countries, especially China. Studying passengers’ travel choices on high-speed railway networks can aid the design of efficient operations and schedule plans. The Tong and Richardson algorithm that is used in this model offers a promising method for finding the optimal path in a schedule-based transit network. However, three aspects of this algorithm limit its application to high-speed railway networks. First, these networks have more complicated common line problems than other transit networks. Without a proper treatment, the optimal paths cannot be found. Second, nonadditive fares are important factors in considering travel choices. Incorporating these factors increases the searching time; improvement in this area is desirable. Third, as high-speed railways have low-frequency running patterns, their passengers may prefer to wait at home or at the office instead of at the station. Thus, consideration of a waiting penalty is needed. This paper suggests three extensions to improve the treatments of these three aspects, and three examples are presented to illustrate the applications of these extensions. The improved algorithm can also be used for other transit systems.

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The different versions of the original document can be found in:

http://downloads.hindawi.com/journals/jat/2017/9216864.xml,
http://dx.doi.org/10.1155/2017/9216864
https://doaj.org/toc/0197-6729,
https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/
http://downloads.hindawi.com/journals/jat/2017/9216864.pdf,
https://core.ac.uk/display/80971150,
http://hub.hku.hk/bitstream/10722/238638/1/content.pdf,
http://hub.hku.hk/handle/10722/237313,
https://scholars.cityu.edu.hk/en/publications/three-extensions-of-tong-and-richardsons-algorithm-for-finding-the-optimal-path-in-schedulebased-railway-networks(b9a77c2b-5d5f-4902-a4ca-e69c4a5f2c34).html,
https://academic.microsoft.com/#/detail/2562410910
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Published on 01/01/2017

Volume 2017, 2017
DOI: 10.1155/2017/9216864
Licence: Other

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