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A recurrent neural network for urban long-term traffic flow forecasting

Belhadi, Asma; Djenouri, Youcef; Djenouri, Djamel; Lin, Jerry Chun-Wei

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Authors

Asma Belhadi

Youcef Djenouri

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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