RETRACTED ARTICLE: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system
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...
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| Veröffentlicht in: | Neural computing & applications 2019-05, Vol.31 (5), p.1665-1677 |
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| container_title | Neural computing & applications |
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| creator | Ai, Yi Li, Zongping Gan, Mi Zhang, Yunpeng Yu, Daben Chen, Wei Ju, Yanni |
| description | 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. |
| doi_str_mv | 10.1007/s00521-018-3470-9 |
| format | Article |
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| subjects | Artificial Intelligence Bicycles Bicycling Business models Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Forecasting Image Processing and Computer Vision Neural networks Probability and Statistics in Computer Science S.I.: Emerging Intelligent Algorithms for Edge-of-Things Computing Supply & demand Tensors Time of use |
| title | RETRACTED ARTICLE: A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system |
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