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MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion

MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal... Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion

Huang, Xiaohui; Jiang, Yuan; Tang, Jie
Applied Intelligence , Volume OnlineFirst – Mar 6, 2023
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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-023-04494-8
Publisher site
See Article on Publisher Site

Abstract

Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms.

Journal

Applied Intelligence – Springer Journals

Published: Mar 6, 2023

Keywords: Multi-attention; Traffic flow prediction; Spatio-temporal

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