MPC Research Reports
Report Details

Title:	Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis
Authors:	Ali Hassandokht Mashhadi, Nikola Markovic, and Abbas Rashidi
University:	University of Utah
Publication Date:	Dec 2023
Report #:	MPC-23-510
Project #:	MPC-669
TRID #:	01907833
Keywords:	computer vision, highway safety, image analysis, machine learning, ranking (statistics), safety analysis

Abstract

Roadside safety is a critical aspect of transportation management, with elements like rigid obstacles, guardrails, clear zones, and side slopes significantly impacting accident outcomes. The Federal Highway Administration (FHWA) provides a valuable rating system for departments of transportation (DOTs), but the manual rating process is time-consuming and prone to inconsistencies. This project introduces an innovative solution employing computer vision and machine learning algorithms to automate the roadside safety evaluation process. Utilizing pretrained models such as VGG16 and images captured from Utah roadways, the research team develops a robust algorithm for automated safety evaluation that aligns with the FHWA rating system, providing a comprehensive and efficient method for assessing roadside conditions. Tailored computer vision algorithms detect specific features, enhancing the accuracy of safety evaluations. Pretrained models for clear zone detection and roadside slope classification further contribute to a nuanced understanding of roadside elements. The project's outcome is a shapefile containing safety rankings for road segments on five state roads. This tool empowers traffic engineers with data-driven insights, enabling informed decision-making for prioritizing improvement projects and enhancing road safety. The automated approach showcased in this research offers a promising avenue for strengthening roadside safety measures and preventing potential accidents. While acknowledging challenges such as periodic retraining and potential false positives, this approach stands as a promising addition to existing methods. The study culminates in a shapefile encompassing safety rankings, roadside features, and illustrative sample images, providing a tangible tool for UDOT in optimizing road safety strategies.

How to Cite

Mashhadi, Ali Hassandokht, Nikola Markovic, and Abbas Rashidi. Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis, MPC-23-510. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2023.