Deep Learning-Based Passenger Flow Hotspot Prediction for Urban Cab Trips

The taxi-cab has been regarded as one of the influential public traffic modes with the characteristics of “point-to-point,” which can provide a high-quality data set for analyzing traffic hotspots. In this study, the authors use the GPS data of cabs in Hejin city, Shanxi Province, to extract the spatio-temporal distribution of traffic hotspots. Afterwards, the deep learning model, LSTM, is developed to predict traffic hotpots by judging the next point of individual trajectories under different weather and period conditions. Results show that the proposed approach has a good prediction accuracy of 97%. The findings can help to guide dispatching cabs, relieve urban traffic pressure, and reduce passengers’ waiting time.

• Record URL:
  • https://doi.org/10.1061/9780784484869.101
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784484869
• Supplemental Notes:
  • © 2023 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Lei, Huiying
  • Wang, Wei
  • Hua, Xuedong
  • Wang, Yongjie
• Conference:
  • 23rd COTA International Conference of Transportation Professionals
  • Location: Beijing , China
  • Date: 2023-7-14 to 2023-7-17
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1050-1060
• Monograph Title: CICTP 2023: Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation

Subject/Index Terms

• TRT Terms: Bottlenecks; High risk locations; Machine learning; Predictive models; Taxi services; Vehicle trajectories
• Geographic Terms: Shanxi (China)
• Subject Areas: Highways; Passenger Transportation; Public Transportation;

Filing Info

• Accession Number: 01894715
• Record Type: Publication
• ISBN: 9780784484869
• Files: TRIS, ASCE
• Created Date: Sep 27 2023 9:10AM