Traffic load simulation on bridges based on improved non-contact machine vision technique and multi-source information fusion

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.