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MVDLSTM: MultiView deep LSTM framework for online ride-hailing order prediction

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Abstract

Online ride-hailing order forecasting is a very important part of the intelligent traffic dispatch system. Accurate order forecasting can reduce the flow of invalid vehicles and improve the user experience of online ride-hailing. We propose a multi-view deep long short-term memory (LSTM) network architecture (MultiView deep LSTM framework), which uses convolutional neural network and graph convolutional network to extract the temporal and spatial characteristics of online ride-hailing orders, obtains the correlation information between regional orders through the order view, regional speed view, and weather factor view, and then uses LSTM unit and attention unit to predict the order volume in real time. We use Didi Haikou, China’s online ride-hailing dataset for training, compare it with the prediction algorithms of other articles, and experiment with different choices of the contrast framework. The experimental results show that our deep learning framework can effectively capture comprehensive spatio-temporal correlation and obtain better results. The model maintained good performance at 15 min, 30 min, and 1 h. Experiments conducted on the actual demand data onto ride-hailing from Didi Haikou data prove that our method is better than the latest method.

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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) and 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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  2. Huyin Zhang
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  3. Cong Li
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  4. Shiming Tao
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  5. Fei Yang
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Correspondence to Huyin Zhang.

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Wu, Y., Zhang, H., Li, C. et al. MVDLSTM: MultiView deep LSTM framework for online ride-hailing order prediction. J Supercomput 78, 8531–8559 (2022). https://doi.org/10.1007/s11227-021-04237-x

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