Abstract
Long-term traffic prediction has tremendous significance for ITS intelligent traffic management security problem. An accurate forecast can improve the efficiency of traffic management and reduce traffic accidents. Thus, the complex dynamic time-space cycle of traffic flow data makes traffic prediction a considerable challenge. Although the existing graph convolution method can capture the correlation between nodes, but never proposed to capture the similarity between the hidden layers of the graph convolution. This paper combines time, space, occupancy, and other related factors to propose a unique multi-period conditional random field (CRF) graph convolution model to accurately predict long-term traffic flow (CRFST-GCN). First, divide the data into three independent fields: trend, day, and week, and then input the data into CRFGCN frame to effectively extract spatial features. The convolution module captures the time-series relationship. Finally, it is verified on two real data sets that our proposed model effectively extracts similarities, and the results show that the model is 40 % more accurate than traditional methods during peak hours.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grants 61976087, 62072170, 61872130, and 61872138, the Fundamental Research Funds for the Central Universities under Grant 531118010527, the Science and Technology Key Projects of Hunan Province (No.2022GK2015) and the Hunan Provincial Natural Science Foundation of China (No.2021JJ30141).
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Diao, C., Zhang, D., Liang, W., Li, KC., Jiang, M. (2022). CRFST-GCN: A Deeplearning Spatial-Temporal Frame to Predict Traffic Flow. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_1
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