Characterizing Passenger Flow in the Hub Based on Mobile Signaling Data: A Case Study on Beijing West Railway Station

摘要

Passenger transportation hub is important for the urban transportation network. However, it's difficult to get the passenger flow pattern within the hub station using traditional method, such as video detection. Mobile signaling data, as a kind of big data provides a solution for this problem. In order to improve the accuracy of extracting travel information, we need to address oscillation sequences and locational uncertainty in mobile signaling data first. And then, we can obtain the travel information. Finally, we use the earlier proposed method to extract the trip information and analyze the temporal and spatial distribution in Beijing West Railway Station, and verify the effectiveness of the method. The method proposed in this paper is beneficial for hub managers to understand the characteristics of passengers' flow and effectively carry on transportation tasks and so on.