Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction


Pengcheng Li, Changjiu Ke, Hongyu Tu, Houbing Zhang, Xu Zhang, Journal of Information Processing Systems Vol. 19, No. 1, pp. 130-138, Feb. 2023  

10.3745/JIPS.04.0266
Keywords: Optimization Graph, Shared Attention, Spatio-temporal Attention, traffic flow forecasting
Fulltext:

Abstract

The traffic flow in an urban area is affected by the date, weather, and regional traffic flow. The existing methods are weak to model the dynamic road network features, which results in inadequate long-term prediction performance. To solve the problems regarding insufficient capacity for dynamic modeling of road network structures and insufficient mining of dynamic spatio-temporal features. In this study, we propose a novel traffic flow prediction framework called shared spatio-temporal attention convolution optimization network (SSTACON). The shared spatio-temporal attention convolution layer shares a spatio-temporal attention structure, that is designed to extract dynamic spatio-temporal features from historical traffic conditions. Subsequently, the graph optimization module is used to model the dynamic road network structure. The experimental evaluation conducted on two datasets shows that the proposed method outperforms state-of-the-art methods at all time intervals.


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Cite this article
[APA Style]
Li, P., Ke, C., Tu, H., Zhang, H., & Zhang, X. (2023). Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction. Journal of Information Processing Systems, 19(1), 130-138. DOI: 10.3745/JIPS.04.0266.

[IEEE Style]
P. Li, C. Ke, H. Tu, H. Zhang, X. Zhang, "Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction," Journal of Information Processing Systems, vol. 19, no. 1, pp. 130-138, 2023. DOI: 10.3745/JIPS.04.0266.

[ACM Style]
Pengcheng Li, Changjiu Ke, Hongyu Tu, Houbing Zhang, and Xu Zhang. 2023. Shared Spatio-temporal Attention Convolution Optimization Network for Traffic Prediction. Journal of Information Processing Systems, 19, 1, (2023), 130-138. DOI: 10.3745/JIPS.04.0266.