research-article

Traffic Flow Prediction via Spatial Temporal Graph Neural Network

Authors:

Jian YuAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 1082 - 1092

https://doi.org/10.1145/3366423.3380186

Published: 20 April 2020 Publication History

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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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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Published: 20 April 2020

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Fan JWeng WChen QWu HWu J(2025)PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow PredictionNeural Networks10.1016/j.neunet.2024.106941183(106941)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2024.106941
Wu YZhang XTang H(2025)A noval Dual-Parameter Structural Model with Enhanced traffic flow representationsNeurocomputing10.1016/j.neucom.2025.129401(129401)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2025.129401
Wang XWang XHuang FZou FLiao LZeng R(2025)DSTF: A Diversified Spatio-Temporal Feature Extraction Model for traffic flow predictionNeurocomputing10.1016/j.neucom.2024.129280621(129280)Online publication date: Mar-2025
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