Asma Belhadi
A recurrent neural network for urban long-term traffic flow forecasting
Belhadi, Asma; Djenouri, Youcef; Djenouri, Djamel; Lin, Jerry Chun-Wei
Authors
Youcef Djenouri
Dr Djamel Djenouri Djamel.Djenouri@uwe.ac.uk
Associate Professor in Computer Science
Jerry Chun-Wei Lin
Abstract
This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, named RNN-LF, is then proposed to predict the long-term of flows from multiple data sources. Moreover, a parallel implementation on GPU of the proposed solution is developed (GRNN-LF), which allows to boost the performance of RNN-LF. Several experiments have been carried out on real traffic flow including a small city (Odense, Denmark) and a very big city (Beijing). The results reveal that the sequential version (RNN-LF) is capable of dealing effectively with traffic of small cities. They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data.
Citation
Belhadi, A., Djenouri, Y., Djenouri, D., & Lin, J. C. (2020). A recurrent neural network for urban long-term traffic flow forecasting. Applied Intelligence, 50(10), 3252-3265. https://doi.org/10.1007/s10489-020-01716-1
Journal Article Type | Article |
---|---|
Acceptance Date | May 16, 2020 |
Online Publication Date | May 16, 2020 |
Publication Date | Oct 1, 2020 |
Deposit Date | Apr 8, 2021 |
Publicly Available Date | Mar 29, 2024 |
Journal | Applied Intelligence |
Print ISSN | 0924-669X |
Electronic ISSN | 1573-7497 |
Publisher | Springer (part of Springer Nature) |
Peer Reviewed | Peer Reviewed |
Volume | 50 |
Issue | 10 |
Pages | 3252-3265 |
DOI | https://doi.org/10.1007/s10489-020-01716-1 |
Keywords | Artificial Intelligence |
Public URL | https://uwe-repository.worktribe.com/output/7249383 |
Additional Information | First Online: 16 May 2020 |
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http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
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