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Title	Traffic Flow Prediction Via Spatial Temporal Graph Neural Network
ID_Doc	58593
Authors	Wang X.; Ma Y.; Wang Y.; Jin W.; Wang X.; Tang J.; Jia C.; Yu J.
Year	2020
Published	The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
DOI	http://dx.doi.org/10.1145/3366423.3380186
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. © 2020 ACM.
Author Keywords	Dynamic; Graph Neural Networks; Recurrent Neural Network; Spatial Temporal Model; Traffic Prediction; Transformer

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