Predicting Real-Time Surge Pricing of Ride-Hailing Companies

Ride-hailing companies such as Uber and Lyft represent a popular and growing mode of transit in cites worldwide. These companies employ surge pricing in real time to balance the needs of both drivers and riders. Surge prices in the next few minutes to hours could be predicted to encapsulate the complex evolution of service fleets and service demand in the short term. Surge pricing, if effectively predicted and disseminated to both drivers and riders, can be used to more efficiently allocate vehicles, save users money and time, and provide profitable insight to drivers, which ultimately helps the optimality and reliability of transportation networks. This paper explores the spatio-temporal correlations among the urban environment, traffic flow characteristics and surge multipliers. The authors propose a general framework for predicting the short-term evolution of surge multipliers in real-time using a linear model with L1 regularization, coupled with pattern clustering. This model is able to predict Uber surge multipliers in the urban areas of Pittsburgh up to two hours in advance using data from the previous hour within 3-5% error, out-performing the overall mean and the historical average. Cross-correlation of Uber and Lyft surge multiplier is also explored.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AP020 Standing Committee on Emerging and Innovative Public Transport and Technologies.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Battifarano, Matthew
    • Qian, Zhen (Sean)
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01698025
  • Record Type: Publication
  • Report/Paper Numbers: 19-04871
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:44AM