Abstract
Traffic flow prediction plays a crucial role in assisting operation of road network and road planning. However, due to the dynamic correlations of road network nodes, the physical connectivity may not reflect the relationship of roads nodes. In this paper, a closed loop based spatial-temporal graph convolution neural networks (CLSTGCN) is proposed by constructing the closed loop with spatial correlation information of road network nodes. The designed model consists of multiple spatial-temporal blocks, which combines the attention mechanism with closed loop correlation information to promote the aggregation in spatial dimensions. Meanwhile, in order to improve the accuracy of long-term prediction, long-term road network trend is supplied into the model, which can capture the temporal features accurately. The experiments on two real world datasets demonstrate that the proposed model outperforms the state of art baselines.
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Acknowledgments
This research was funded by the Natural Science Foundation of Shandong Province for Key Project under GrantZR2020KF006, the National Natural Science Foundation of China under Grant 62273164, the Development Program Project of Youth Innovation Team of Institutions of Higher Learning in Shandong Province, and the Project of Shandong Province Higher Educational Science and Technology Program under Grants J16LB06 and J17KA055.
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Li, H., Han, S., Zhao, J., Lian, Y., Yu, W., Yang, X. (2023). CLSTGCN: Closed Loop Based Spatial-Temporal Convolution Networks for Traffic Flow Prediction. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_55
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DOI: https://doi.org/10.1007/978-981-99-4755-3_55
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