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Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand

Author

Listed:
  • 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-Center, Ningbo 315211, China)

  • Siteng Li

    (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-Center, 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-Center, Ningbo 315211, China)

  • Zhenlei Chen

    (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-Center, Ningbo 315211, China)

Abstract

As a flourishing basic transportation service in recent years, online car-hailing has made great achievements in metropolitan cities. Accurate spatiotemporal forecasting plays a significant role in the deployment of a network for online car-hailing demand services. A self-attention mechanism in convolutional long short-term memory (ConvLSTM) is proposed to accurately predict the online car-hailing demand. It can more effectively address the disadvantage that ConvLSTM is not good at capturing spatial correlation over a large spatial extent. Furthermore, it can generate features by aggregating pair-wise similarity scores of features at all positions of input and memory, and thus obtain the function of long-range spatiotemporal dependencies. First, the online car-hailing trajectories dataset was converted into images after geographic grid matching, and image enhancement was performed by cropping. Then, the effectiveness of the ConvLSTM embedded with a self-attention mechanism (SA-ConvLSTM) was demonstrated by comparing it to existing models. The experimental results showed that the proposed model performed better than the existing models, and including spatiotemporal information in images would perform better predictions than including spatial information in time-series pixels.

Suggested Citation

  • Hongxia Ge & Siteng Li & Rongjun Cheng & Zhenlei Chen, 2022. "Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7371-:d:840351
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    References listed on IDEAS

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