Research on traffic flow prediction method based on deep learning

Zhen Yu Zhao

doi:10.1117/12.3026490

27 March 2024 Research on traffic flow prediction method based on deep learning

Zhen Yu Zhao

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131051H (2024) https://doi.org/10.1117/12.3026490
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

With the widespread development of machine learning and deep learning technology in recent years, artificial intelligence technology has been widely applied in various fields. Traffic flow prediction has been a hot research direction in recent years. Traffic flow is an important influencing factor in the field of transportation and travel, playing an important role in the rational allocation of transportation resources and ensuring the smooth performance of transportation and travel. It is necessary to predict future traffic flow based on historical travel data. Some existing deep learning methods require high time complexity and high hardware costs, and that have some shortcomings in prediction ability. This article proposes a CATformer model combining convolutional neural networks and attention mechanisms to solve the traffic flow prediction problem in time series problems, extracting and fusing features from multiple vector spaces for traffic data, The experimental results of predicting the flow of future traffic nodes show that the CATformer model has improved prediction accuracy compared to the benchmark method, achieving the task of traffic node flow prediction based on time series.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhen Yu Zhao "Research on traffic flow prediction method based on deep learning", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131051H (27 March 2024); https://doi.org/10.1117/12.3026490

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