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07-04-2018 | S.I.: Emerging Intelligent Algorithms for Edge-of-Things Computing | Issue 5/2019

Neural Computing and Applications 5/2019

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

Journal:
Neural Computing and Applications > Issue 5/2019
Authors:
Yi Ai, Zongping Li, Mi Gan, Yunpeng Zhang, Daben Yu, Wei Chen, Yanni Ju

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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