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20 February 2024 Research on freeway traffic flow prediction method based on Att-Conv-LSTM model
Jingxue Guo, Xianyu Wu
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Proceedings Volume 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023); 130640C (2024) https://doi.org/10.1117/12.3015894
Event: 7th International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 2023, Dalian, China
Abstract
Accurate prediction of traffic flow is the basis for realizing intelligent transportation systems. It is challenging to achieve accurate prediction of highway traffic flow because of the characteristics of highway traffic state evolution such as temporal non-linearity and spatial heterogeneity. In this paper, a Conv-LSTM freeway traffic flow prediction model based on an attention mechanism is proposed, which can automatically extract the inherent features of historical traffic flow data. First, the convolution and Long Short-Term Memory model are combined to form a Conv-LSTM module based on the attention mechanism, which could take out the time-space features of the traffic flow data. The attentional mechanisms are designed to identify the importance of different flow series. In addition, the Bi-LSTM module is used to analyze the historical traffic flow data to capture the trend of traffic flow in the forward and backward directions to extract the daily and weekly traffic flow cycle features. Finally, the results show a better prediction performance realized by the proposed integrated model compared to other available approaches.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jingxue Guo and Xianyu Wu "Research on freeway traffic flow prediction method based on Att-Conv-LSTM model", Proc. SPIE 13064, Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023), 130640C (20 February 2024); https://doi.org/10.1117/12.3015894
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KEYWORDS
Data modeling
Deep learning
Feature extraction
Transportation
Convolutional neural networks
Machine learning
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