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

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

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.

• 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: Apr 9, 2021
• 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