Analysis of subway users' behaviour-based on the latent class regression
by Li-hua Liu; Ming-lei Song; Jian-rong Liu; Xue-jiao Wang; Ming-hui Wang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 14, No. 3, 2021

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.

Online publication date: Thu, 23-Dec-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com