Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks

Predicting taxi demand throughout a city can help to organize the taxi fleet and minimize the wait-time for passengers and drivers. In this paper, they propose a sequence learning model that can predict future taxi requests in each area of a city based on the recent demand and other relevant information. Remembering information from the past is critical here, since taxi requests in the future are correlated with information about actions that happened in the past. For example, someone who requests a taxi to a shopping center, may also request a taxi to return home after few hours. They use one of the best sequence learning methods, long short term memory that has a gating mechanism to store the relevant information for future use. The authors evaluated their method on a data set of taxi requests in New York City by dividing the city into small areas and predicting the demand in each area. They show that this approach outperforms other prediction methods, such as feed-forward neural networks. In addition, they show how adding other relevant information, such as weather, time, and drop-offs affects the results.

• Record URL:
  • https://doi.org/10.1109/TITS.2017.2755684
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Xu, Jun
  • Rahmatizadeh, Rouhollah
  • Bölöni, Ladislau
  • Turgut, Damla
• Publication Date: 2018-8

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 2572-2581
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Mathematical prediction; Neural networks; Simulation; Taxi services; Taxicabs; Time series; Traffic forecasting; Travel demand
• Geographic Terms: New York (New York)
• Subject Areas: Highways; Passenger Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01679879
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Aug 31 2018 10:19AM