Data-Driven Multi-Step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Yi Hou, Chao Yang, Matthew Barth

Research output: Contribution to conferencePaper

17 Scopus Citations


Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 min. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps.
Original languageAmerican English
Number of pages12
StatePublished - 2019
Event2019 Computer Vision Conference (CVC) - Las Vegas, Nevada
Duration: 25 Apr 201926 Apr 2019


Conference2019 Computer Vision Conference (CVC)
CityLas Vegas, Nevada

NREL Publication Number

  • NREL/CP-5400-72858


  • convolutional neural network
  • demand prediction
  • ride-hailing


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