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Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model

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  • Qianru Qi

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China
    National Traffic Management Engineering and Technology Research Centre, Ningbo University Sub-Centre, Ningbo 315211, China)

  • Rongjun Cheng

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China
    National Traffic Management Engineering and Technology Research Centre, Ningbo University Sub-Centre, Ningbo 315211, China)

  • Hongxia Ge

    (Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
    Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, Nanjing 210096, China
    National Traffic Management Engineering and Technology Research Centre, Ningbo University Sub-Centre, Ningbo 315211, China)

Abstract

In recent years, online ride-hailing has become an indispensable part of residents’ travel mode. Therefore, the prediction of online ride-hailing travel demand has become extremely important. In the era of big data, the application of big data in the field of transportation is becoming more extensive. Based on the open data of ride-hailing trips in Haikou City, Hainan Province, provided by the Didi platform and combined with the rainfall data of Haikou City, this paper proposes a gate recurrent unit (GRU) model considering rainfall factors and rest days factors for short-term trip demand prediction. The K-fold cross-validation method is adopted to adjust the parameters of the model to the optimal ones through the training set. The improved GRU model is compared with the original GRU model and other classic models, and the model is evaluated by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R 2 score indexes. Finally, it is proved that the GRU model proposed in this paper greatly improves the prediction accuracy of short-term online ride-hailing travel demand.

Suggested Citation

  • Qianru Qi & Rongjun Cheng & Hongxia Ge, 2022. "Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4083-:d:782995
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    References listed on IDEAS

    as
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