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Spatiotemporal Prediction of Urban Online Car-Hailing Travel Demand Based on Transformer Network

Author

Listed:
  • Shuoben Bi

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Cong Yuan

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Shaoli Liu

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Luye Wang

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Lili Zhang

    (School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Online car-hailing has brought convenience to daily travel, whose accurate prediction benefits drivers and helps managers to grasp the characteristics of urban travel, so as to facilitate decisions. Spatiotemporal prediction in the transportation field has usually been based on a recurrent neural network (RNN), which has problems such as lengthy computation and backpropagation. This paper describes a model based on a Transformer, which has shown success in computer vision. The study area is divided into grids, and the structure of travel data is converted into video frames by time period, based on predicted spatiotemporal travel demand. The predictions of the model are closest to the real data in terms of spatial distribution and travel demand when the data are divided into 10 min intervals, and the travel demand in the first two hours is used to predict demand in the next hour. We experimentally compare the proposed model with the three most commonly used spatiotemporal prediction models, and the results show that our model has the best accuracy and training speed.

Suggested Citation

  • Shuoben Bi & Cong Yuan & Shaoli Liu & Luye Wang & Lili Zhang, 2022. "Spatiotemporal Prediction of Urban Online Car-Hailing Travel Demand Based on Transformer Network," Sustainability, MDPI, vol. 14(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13568-:d:948609
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    References listed on IDEAS

    as
    1. Wu, Tian & Wang, Shouyang & Wang, Lining & Tang, Xiao, 2022. "Contribution of China's online car-hailing services to its 2050 carbon target: Energy consumption assessment based on the GCAM-SE model," Energy Policy, Elsevier, vol. 160(C).
    2. Alberto Mozo & Bruno Ordozgoiti & Sandra Gómez-Canaval, 2018. "Forecasting short-term data center network traffic load with convolutional neural networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-31, February.
    3. Xiaohui Huang & Jie Tang & Zhiying Peng & Zhiyi Chen & Hui Zeng & Rahib Abiyev, 2022. "A Sparse Gating Convolutional Recurrent Network for Traffic Flow Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-14, February.
    4. Yuhan Guo & Yu Zhang & Youssef Boulaksil & Ning Tian, 2022. "Multi-dimensional spatiotemporal demand forecasting and service vehicle dispatching for online car-hailing platforms," International Journal of Production Research, Taylor & Francis Journals, vol. 60(6), pages 1832-1853, March.
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