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10 August 2023 Short-term traffic flow prediction based on TDG of expressway
Ruoxi Du, Chuanqi Liu, Hongtao Du, Jiangfeng Wang
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Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 1275935 (2023) https://doi.org/10.1117/12.2686578
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China
Abstract
In order to solve the problem that the current short-term traffic flow prediction algorithm ignores the multi-dimension of traffic flow data, which leads to low prediction accuracy, this paper introduced the tensor decomposition theory and established a dynamic tensor model based on the multi-time dimensions of week, day and period. This paper designed a short-term traffic flow prediction algorithm based on Tucker Decomposition on Gradient (TDG), and took the traffic flow velocity data of Dujiakan section of Beijing-Hong Kong-Macau Expressway as an example for empirical verification. The result shows that the algorithm can quickly achieve good prediction results based on less historical data, and can effectively achieve traffic flow prediction for weekdays and weekends, and the average mean absolute error (MAE) is about 4.4%. Compared with the classical prediction algorithm, it can show better prediction accuracy.
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Ruoxi Du, Chuanqi Liu, Hongtao Du, and Jiangfeng Wang "Short-term traffic flow prediction based on TDG of expressway", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 1275935 (10 August 2023); https://doi.org/10.1117/12.2686578
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KEYWORDS
Data modeling
Matrices
Transportation
Design and modelling
Intelligence systems
Mathematical optimization
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