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A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system

  • S.I.: Emerging Intelligent Algorithms for Edge-of-Things Computing
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Abstract

Dockless bike-sharing is becoming popular all over the world, and short-term spatiotemporal distribution forecasting on system state has been further enlarged due to its dynamic spatiotemporal characteristics. We employ a deep learning approach, named the convolutional long short-term memory network (conv-LSTM), to address the spatial dependences and temporal dependences. The spatiotemporal variables including number of bicycles in area, distribution uniformity, usage distribution, and time of day as a spatiotemporal sequence in which both the input and the prediction target are spatiotemporal 3D tensors within one end-to-end learning architecture. Experiments show that conv-LSTM outperforms LSTM on capturing spatiotemporal correlations.

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Authors and Affiliations

  1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China

    Yi Ai, Zongping Li, Mi Gan, Daben Yu, Wei Chen & Yanni Ju

  2. Comprehensive Transportation Key Laboratory of Sichuan Province, Chengdu, China

    Yi Ai, Zongping Li, Daben Yu, Wei Chen & Yanni Ju

  3. College of Technology, University of Houston, Houston, USA

    Yunpeng Zhang

Authors
  1. Yi Ai
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  2. Zongping Li
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  3. Mi Gan
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  4. Yunpeng Zhang
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  5. Daben Yu
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  6. Wei Chen
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  7. Yanni Ju
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Correspondence to Mi Gan.

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Ai, Y., Li, Z., Gan, M. et al. A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput & Applic 31, 1665–1677 (2019). https://doi.org/10.1007/s00521-018-3470-9

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  • DOI: https://doi.org/10.1007/s00521-018-3470-9

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