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Proceedings Paper • Open Access

Spatio-temporal multi-attention graph network for traffic forecasting
Author(s): Qinzheng Li; Wenxing Zhu

Paper Abstract

Traffic forecasting is one of the most important problems in the areas of intelligent transportation system, and it is the key link. It plays a major role in transportation service and navigation. However, urban traffic has its own characteristics, and the complex traffic system is highly nonlinear and stochastic, which makes traffic forecasting a very difficult problem. Although many previous methods can make the high performance for predicting in traffic forecasting, the existing research has not fully utilized the influence of spatial and temporal characteristics on prediction. In this article, we put forward a new model called Spatio-temporal multi-attention graph network. Taking into account the similar features of traffic flow every day and the interaction between road network structures, the model takes advantages of the internal dependence between the dynamic spatial network and the time dimension information to improve accuracy of forecasting. Experimental results show that our model is nicer over the others, which has good performance and gain more precision prediction accuracy.

Paper Details

Date Published: 24 May 2022
PDF: 7 pages
Proc. SPIE 12260, International Conference on Computer Application and Information Security (ICCAIS 2021), 122600F (24 May 2022); doi: 10.1117/12.2637523
Show Author Affiliations
Qinzheng Li, Shandong University (China)
Wenxing Zhu, Shandong University (China)

Published in SPIE Proceedings Vol. 12260:
International Conference on Computer Application and Information Security (ICCAIS 2021)
Yingfa Lu; Changbo Cheng, Editor(s)

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