Traffic load simulation on bridges based on improved non-contact machine vision technique and multi-source information fusion. (31st May 2023)

• Record Type: Journal Article
• Authors: Yin, Xinfeng; Huang, Zhou; Liu, Yang
• Is Part Of: Measurement. Volume 213(2023)
• Publication Date: 2023-05-31
• Subjects: Traffic load simulation -- Non-contact machine vision -- Multi-source information fusion -- Monte Carlo-Intelligent Driving Model -- Traffic measurements -- Long-span bridge; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2023.112754
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: This study proposed a traffic flow framework without the assistance of WIM systems. Detection accuracy of machine vision is improved by a k-means++ clustering algorithm. Robustness and stability of the vehicle database are ensured by information fusion. Traffic flow model based on MC-IDM has macroscopic and microscopic traffic features. Effectiveness of the framework is verified by a field test of a suspension bridge. Abstract: This study presented a novel traffic load simulation method. Specifically, an improved You Only Look Once (YOLO) detector based on the k-means++ clustering algorithm is implemented to identify vehicle types, and the multi-objective tracking algorithm is used to obtain the spatial location and velocity information of vehicles. Based on the results of detection and tracking, multi-source information fusion techniques are applied to build a vehicle database. Meanwhile, a Monte Carlo-Intelligent Driving Model can be developed for traffic load simulation based on the vehicle database. The applicability and effectiveness of the method are verified by the suspension bridge field test. The results show that the identification accuracy of vehicle type and vehicle number is about 88.28% and 74.09%, respectively. Meanwhile, the vehicle speed error is less than 6.5%. The method can be used to provide a reasonable traffic load model for large-span bridges without the aid of a deck-embedded weigh-in-motion system.