Study on the subway transfer recognition during rush hour based on big data

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.