Journal
NEURAL COMPUTING & APPLICATIONS
Volume 31, Issue 5, Pages 1665-1677Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3470-9
Keywords
Dockless bike-sharing system; Short-term spatiotemporal distribution forecasting; Deep learning; Convolutional long short-term memory network (conv-LSTM)
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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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