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Urban ride-hailing demand prediction with multi-view information fusion deep learning framework

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Abstract

Urban online ride-hailing demand forecasting is an important component of smart city transportation systems. An accurate online ride-hailing demand prediction model can help cities allocate online ride-hailing resources reasonably, reduce energy waste, and reduce traffic congestion. With the massive popularity of online ride-hailing, we can collect a large amount of order data, and how to use deep learning models for improving order prediction accuracy has become a hot research topic. Most of the urban online taxi demand forecasting methods do not sufficiently consider the influencing factors and cannot model the complex nonlinear spatio-temporal relationships. Therefore, we propose a multi-view deep spatio-temporal network framework (MVDSTN) architecture to obtain the spatio-temporal relationships for online ride-hailing demand prediction. Our proposed model includes five views,up-passenger order view, down-passenger order view, POI view, spatial GCN view, POI view and weather view, applies LSTM with attention mechanism to achieve demand prediction for urban online taxi bodies. Experiments Haikou Didi Taxi datasets and Wuhan Taxi datasets prove that our model has good robustness and the prediction method outperforms current methods.

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Data Availability

The data that support the findings of this study are available from [DDT’s ”Gaia Project”] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [DDT’s ”Gaia Project”].

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61772386), Natural Science Foundation of Hubei Province(Grant No. 2020CFB795), Wuhan Institute of City Research Project(Grant No. 2018CYZDKY007)

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Yonghao Wu, Huyin Zhang, Cong Li, Shiming Tao & Fei Yang

  2. Department of Information Engineering, Wuhan Institute of City, Wuhan, China

    Yonghao Wu

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  1. Yonghao Wu
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Correspondence to Huyin Zhang.

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Wu, Y., Zhang, H., Li, C. et al. Urban ride-hailing demand prediction with multi-view information fusion deep learning framework. Appl Intell 53, 8879–8897 (2023). https://doi.org/10.1007/s10489-022-03966-7

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