Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

• Record URL:
  https://doi.org/10.3141/2634-10
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309441520
• Authors:
  • Li, Yunxuan
  • Lu, Jian
  • Zhang, Lin
  • Zhao, Yi
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 57–68
• Monograph Title: Developing Countries
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2634
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Classification; Mobile applications; Taxicabs; Traffic forecasting; Travel demand
• Subject Areas: Data and Information Technology; Public Transportation;

Filing Info

• Accession Number: 01620113
• Record Type: Publication
• ISBN: 9780309441520
• Report/Paper Numbers: 17-02403
• Files: TRIS, TRB, ATRI
• Created Date: Dec 29 2016 3:53PM