Title: Analysis of subway users' behaviour-based on the latent class regression

Authors: Li-hua Liu; Ming-lei Song; Jian-rong Liu; Xue-jiao Wang; Ming-hui Wang

Addresses: School of Civil and Transportation Engineering, Henan University of Urban Construction, Pingdingshan 467036, China ' School of Civil and Transportation Engineering, Henan University of Urban Construction, Pingdingshan 467036, China ' School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China ' School of Civil and Transportation Engineering, Henan University of Urban Construction, Pingdingshan 467036, China ' Wujiang District People's Government Office of Suzhou, Jiangsu Province, Suzhou 215200, China

Abstract: Since that there is difference of satisfaction and willing-to-travel-by-subway among different subway users, a task one should take into account of is how to classify subway users effectively, and analyse factors affect the satisfaction and willing-to-travel-by-subway of different subgroups of subway users, respectively. Based on subway users' satisfaction of the subway and their travelling behaviour, this paper classifies subway users with the latent class regression model. This result shows that subway users should be classified into three subgroups: the neutral passengers, the satisfied passengers, and the loyal passengers, also it is found that private car ownership, accessibility of the subway station, price evaluation and speed evaluation have a great influence on the classification. Based on the classification, this paper regresses the overall satisfaction of the subway, the recommendation of the subway, and the frequency of travelling by subway on the factors, respectively. It is found that there are non-negligible differences of factors' parameters on the three subgroups.

Keywords: urban traffic; subway; classification; latent class regression; regression analysis.

DOI: 10.1504/IJCSM.2021.119902

International Journal of Computing Science and Mathematics, 2021 Vol.14 No.3, pp.274 - 285

Received: 29 Mar 2019
Accepted: 07 May 2019
Published online: 23 Dec 2021 *

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