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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2019

07.04.2018 | S.I.: Emerging Intelligent Algorithms for Edge-of-Things Computing

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

verfasst von: Yi Ai, Zongping Li, Mi Gan, Yunpeng Zhang, Daben Yu, Wei Chen, Yanni Ju

Erschienen in: Neural Computing and Applications | Ausgabe 5/2019

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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.
Vorheriger Artikel A new binary salp swarm algorithm: development and application for optimization tasks

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Metadaten
Titel
A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system
verfasst von
Yi Ai
Zongping Li
Mi Gan
Yunpeng Zhang
Daben Yu
Wei Chen
Yanni Ju
Publikationsdatum
07.04.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3470-9

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