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

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-...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2020-10, Vol.50 (10), p.3252-3265
Main Authors: Belhadi, Asma, Djenouri, Youcef, Djenouri, Djamel, Lin, Jerry Chun-Wei
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Machines
Manufacturing
Mechanical Engineering
Neural networks
Processes
Recurrent neural networks
Software
Software development tools
Traffic flow
Weather
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Summary: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.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-01716-1