Study on the subway transfer recognition during rush hour based on big data
by Shushen Yao; Xiaoxiong Weng
International Journal of Information and Communication Technology (IJICT), Vol. 16, No. 1, 2020

Abstract: With the development of the subway network, multipath coexistence becomes very common in big cities. It's followed that the tickets clearing problem is highly concerned by co-investors, which relies on accurate transfer paths identification. Different from the commonly used Logit models for subway transfer recognition problem, we adopted the adaptive Gauss cloud transformation (A-GCT) model, which transformed the distribution of passengers' trip time into multiple concepts of different granularity and evaluated the maturity of the concept by the of parameter named confusion degree (CD). The case in this paper shows that, the A-GCT model has higher accuracy in dealing with uncertain problem such as subway transfer recognition.

Online publication date: Thu, 13-Feb-2020

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 Information and Communication Technology (IJICT):
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