Research on traffic probabilistic forecasting with spatial-temporal graph neural networks

Xingmei Yang; Xin Zhou; Yonghong Chen; Linghan Yao; JianPing Xing

doi:10.1117/12.2636405

6 May 2022 Research on traffic probabilistic forecasting with spatial-temporal graph neural networks

Xingmei Yang, Xin Zhou, Yonghong Chen, Linghan Yao, JianPing Xing

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217620 (2022) https://doi.org/10.1117/12.2636405
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Existing Spatio-temporal graph models consider traffic flow prediction as a "node-level" task, and it is difficult to make decisions based on the results of point prediction alone. The modeling idea is to explore the hidden graph structure that better reflects the spatial dependency of the road network from the traffic flow "signal" to replace the less accurate graph structure that is calculated directly based on the geographical location. Here, we propose the Dilated SpatioTemporal Graph Convolutional Network (DSTGCN) model and the Adaptive Graph Recurrent Neural Network (AGRNN) model for traffic flow prediction. The main work is to propose a self-learning transfer matrix for directed graphs that can automatically discover potential graph structures from the historical traffic of each node, improve the gating mechanism in temporal convolution, and introduce probabilistic prediction into the traffic flow prediction problem to enhance the dimensionality of information used for decision making. DSTGCN and AGRNN are evaluated on two publicly available traffic datasets. METRLA and PEMS-BAY were evaluated and achieved the fastest computational speed (compared to other Spatio-temporal graph models) and the best prediction results, respectively.

Citation Download Citation

Xingmei Yang, Xin Zhou, Yonghong Chen, Linghan Yao, and JianPing Xing "Research on traffic probabilistic forecasting with spatial-temporal graph neural networks", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217620 (6 May 2022); https://doi.org/10.1117/12.2636405

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KEYWORDS

Convolution

Neural networks

Roads

Data modeling

Performance modeling

Sensors

Computer programming

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