Intelligent Transportation Systems and Virtual Reality as Technology Accelerators for Smart Infrastructure Monitoring
type: CreativeWork
description: This work extends the application of intelligent transportation systems (ITS) to the field of structural health monitoring (SHM) for bridge infrastructures and develops a virtual reality (VR) platform as a technology accelerator. In the proposed SHM method, video recordings of traffic are captured using traffic cameras and processed using a combination of YOLO and Deep SORT models, along with projective mapping, to extract the time history of vehicle tire locations. This step, referred to as traffic tracking, predicts the points at which traffic loads excite the bridge. This information is integrated with the bridge's vibration response and the finite element (FE) model to develop a digital twin of the bridge. The necessary models for the traffic tracking component are trained using Google Open Images and tested in a VR environment that simulates traffic on a roadway with installed cameras. Utilizing VR instead of real-world tests significantly accelerates technology development. After achieving promising results in the VR environment, the prototype of the proposed technology has been developed and deployed on a real-world bridge.
abstract: This work extends the application of intelligent transportation systems (ITS) to the field of structural health monitoring (SHM) for bridge infrastructures and develops a virtual reality (VR) platform as a technology accelerator. In the proposed SHM method, video recordings of traffic are captured using traffic cameras and processed using a combination of YOLO and Deep SORT models, along with projective mapping, to extract the time history of vehicle tire locations. This step, referred to as traffic tracking, predicts the points at which traffic loads excite the bridge. This information is integrated with the bridge's vibration response and the finite element (FE) model to develop a digital twin of the bridge. The necessary models for the traffic tracking component are trained using Google Open Images and tested in a VR environment that simulates traffic on a roadway with installed cameras. Utilizing VR instead of real-world tests significantly accelerates technology development. After achieving promising results in the VR environment, the prototype of the proposed technology has been developed and deployed on a real-world bridge.
author: Malekghaini, Niloofar
language: en_US
language: English
keywords: Computer Vision, Intelligent Transportation Systems, Machine Learning, Traffic Tracking
date published: 2024
date modified: 2026-07-27T21:46:52Z
provider: University of Nevada - Reno
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Nevada - Reno