In response to the problem that the analysis of highway congestion in hourly units fails to accurately reflect real-time service levels, this study investigates a method for real-time grading of traffic congestion levels on highways based on video surveillance. The research employs YOLOv5 and the Deep SORT algorithm to extract vehicle trajectories and compute short-term traffic flow macro parameters (including flow rate, density, and speed) and micro parameters such as headway intervals on a one-minute basis. Using the entropy method, weights are assigned to indicators that characterize congestion states to construct a comprehensive congestion measurement index. This index is then used for real-time congestion level classification through Fuzzy C-Means (FCM) clustering, with validation performed using K-means algorithm. The results indicate that the proposed comprehensive congestion measurement index outperforms congestion indicators constructed solely from macro and micro parameters, providing a real-time assessment of traffic flow congestion status for different time periods.
Real-Time Classification of Freeway Traffic Congestion Levels Based on Surveillance Video
Lect. Notes Electrical Eng.
International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024
The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation ; Kapitel : 20 ; 179-187
16.03.2025
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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