A dynamic spatial-temporal attention ensemble model for traffic flow prediction

Jiakun Zhang; Guoyan Yang

doi:10.1117/12.2646821

7 September 2022 A dynamic spatial-temporal attention ensemble model for traffic flow prediction

Jiakun Zhang, Guoyan Yang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 1232909 (2022) https://doi.org/10.1117/12.2646821
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

With the advent of the era of big data, a large number of sensors are scattered on traffic roads, generating traffic flow data, posing huge challenges to traffic flow prediction. Therefore, capturing the spatial features of traffic flow data is greatly significant to improve the accuracy of traffic flow predicted models. Analysis of spatial and temporal characteristics of traffic flow data, this paper proposes a Spatial-Temporal Attention (MA-STLGAT) model to predict the traffic speed in the traffic network. The Graph Attention Network (GAT) assigns different weights to neighbor nodes to capture the dynamic topology structure. Learning the temporal correlation of traffic flow data through Long ShortTerm Memory network (LSTM), and Multi-Head Attention is introduced to learn the contribution of nodes, improve the convergence speed and save computing costs. The experimental results on real data set PeMSD7 show that the predict performance of MA-STLGAT is better than other baseline models.

Citation Download Citation

Jiakun Zhang and Guoyan Yang "A dynamic spatial-temporal attention ensemble model for traffic flow prediction", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 1232909 (7 September 2022); https://doi.org/10.1117/12.2646821

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KEYWORDS

Data modeling

Performance modeling

Roads

Head

Feature extraction

Convolutional neural networks

Data communications

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