Reproduction of spatial–temporal distribution of traffic loads on freeway bridges via fusion of camera video and ETC data

Abstract

Understanding the spatial–temporal distribution of traffic loads on bridge decks is essential for managing traffic flow and assessing bridge load effects. While computer vision can already identify on-bridge vehicle parameters through traffic surveillance cameras, obtaining cost-effective vehicle load information remains a challenge, even though it is the most important service load for bridges. In this study, we present a methodology that applies computer vision algorithms to fuzzy match video-identified data with electronic toll collection (ETC) recorded data to reproduce the spatial–temporal distribution of traffic loads on freeway bridges. The proposed method involves three critical steps. First, multi-object detection and trajectory tracking of vehicle contours, vehicle heads, vehicle tires, and vehicle license plates are implemented using YOLOv5 and DeepSort algorithms. Second, the conversion of image coordinates to real-world coordinates is automatically calibrated using pre-measured roadside reflection points. Third, vehicle license plate characters are identified by the LPRnet algorithm and the results are fused with highway ETC records to determine vehicle weight information, while the KNN clustering algorithm is used to further infer vehicle axle weight. After these three steps, the vehicle load sequence in the video surveillance area can be reproduced. Finally, the effectiveness and feasibility of the proposed method are verified in an example of a highway bridge.