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

Passenger transportation hub is important for the urban transportation network. However, it is 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, the oscillation sequences and locational uncertainty in mobile signaling data need to be addressed first. And then, the travel information can be obtained. Finally, the authors 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.

• Record URL:
  • https://doi.org/10.1061/9780784483053.071
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483053
• Supplemental Notes:
  • © 2020 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhao, Huaiping
  • Shi, Jianjun
  • Sun, Haodong
  • Liu, Di
• Conference:
  • 20th COTA International Conference of Transportation Professionals
  • Location: Xi’an , China
  • Date: 2020-8-14 to 2020-8-16
• Publication Date: 2020

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 841-852
• Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility

Subject/Index Terms

• TRT Terms: Passengers; Pedestrian flow; Railroad stations; Trip distribution
• Geographic Terms: Beijing (China)
• Subject Areas: Operations and Traffic Management; Passenger Transportation; Railroads;

Filing Info

• Accession Number: 01767370
• Record Type: Publication
• ISBN: 9780784483053
• Files: TRIS, ASCE
• Created Date: Mar 22 2021 10:34AM