Region-based demand forecasting in bike-sharing systems using a multiple spatiotemporal fusion neural network-Reference-Cited by-同舟云学术

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Region-based demand forecasting in bike-sharing systems using a multiple spatiotemporal fusion neural network

Published:2022-12-02 Issue:8 Volume:27 Page:4579-4592
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Yan Xiao,Kou Gang,Xiao Feng,Zhang Dapeng,Gan Xianghua^ORCID

Funder

National Natural Science Foundation of China

State Key R & D Program of China

Major Project of the National Social Science Foundation of China

National Science Fund for Distinguished Young Scholars

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-022-07691-8.pdf

Reference34 articles.

1. Ai Y, Li Z, Gan M et al (2019) A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput Appl 31(5):1665–1677

2. Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci 577:852–870

3. Alvarez-Valdes R, Belenguer JM, Benavent E et al (2016) Optimizing the level of service quality of a bike-sharing system. Omega 62:163–175

4. Ashqar HI, Elhenawy M, Almannaa MH, et al (2017) Modeling bike availability in a bike-sharing system using machine learning. In: 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS), IEEE, pp 374–378

5. Caggiani L, Camporeale R, Ottomanelli M et al (2018) A modeling framework for the dynamic management of free-floating bike-sharing systems. Transp Res C: Emerg Technol 87:159–182

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