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Cited by 651Traffic Prediction and Management TechniquesTraffic control and managementAdvanced Graph Neural Networks

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Traffic Flow Prediction via Spatial Temporal Graph Neural Network

Open access
Wang, Xiaoyang, Ma, Yao, Wang, Yiqi, Jin, Wei, Wang, Xin, Tang, Jiliang, Jia, Caiyan, Yu, Jian·2020·Proceedings of The Web Conference 2020
DOI 10.1145/3366423.3380186
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Abstract

Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation networks. The dynamic of the traffic flow on one road not only depends on the sequential patterns in the temporal dimension but also relies on other roads in the spatial dimension. Although there are existing works on predicting the future traffic flow, the majority of them have certain limitations on modeling spatial and temporal dependencies. In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns. In particular, the framework offers a learnable positional attention mechanism to effectively aggregate information from adjacent roads. Meanwhile, it provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies. Experimental results on various real traffic datasets demonstrate the effectiveness of the proposed framework.

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Metadata

Authors
Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., & Yu, J.
Affiliations
  • Beijing Jiaotong University (CN)
  • Michigan State University (US)
ORCID
Venue
Proceedings of The Web Conference 2020
Publisher
ACM
Published
2020-04-20
Pages
1082-1092
DOI
10.1145/3366423.3380186
OpenAlex
W3012562343
Cited by
644
References
33
Citations by year
2026: 42 · 2025: 141 · 2024: 145 · 2023: 134 · 2022: 113 · 2021: 65 · 2020: 3
FWCI
131.32
Type
conference paper
Language
en
Open access
Licence
Locations
International World Wide Web Conference (WWW) (publishedVersion, cc-by)
Topics
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Keywords
traffic flow prediction, spatio-temporal graph neural networks, spatial dependence, temporal dependencies, traffic dynamics, smart cities, transportation networks

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