Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis

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 Department 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 pre-trained 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. Pre-trained 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 Utah Department of Transportation (UDOT) in optimizing road safety strategies.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1151
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/73316
• Summary URL:
  https://rosap.ntl.bts.gov/view/dot/73729
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Utah, Salt Lake City

  Department of Civil & Environmental Engineering
  Salt Lake City, UT  United States  84112

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Utah Department of Transportation

  Salt Lake City, UT  United States 
• Authors:
  • Mashhadi, Ali Hassandokht
  • Markovic, Nikola
  • Rashidi, Abbas
• Publication Date: 2023-12

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 46p

Subject/Index Terms

• TRT Terms: Computer vision; Highway safety; Image analysis; Machine learning; Ranking (Statistics); Safety analysis
• Geographic Terms: Utah
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01907833
• Record Type: Publication
• Report/Paper Numbers: MPC-669, MPC 23-510
• Files: UTC, NTL, TRIS, USDOT, STATEDOT
• Created Date: Feb 12 2024 10:31AM