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

Details

• Creators:
  Mashhadi, Ali Hassandokht ; Markovic, Nikola ; Rashidi, Abbas
• Corporate Creators:
  University of Utah ; Mountain-Plains Consortium
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Automation Computer Vision Highway Safety Image Analysis Machine Learning Roadside Rural Highways Video
• Publication/ Report Number:
  MPC-669;MPC 23-510
• Resource Type:
  Tech Report
• Geographical Coverage:
  Utah;United States
• Edition:
  Final Report
• Corporate Publisher:
  Mountain-Plains Consortium
• 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 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 UDOT in optimizing road safety strategies.
• Format:
  PDF
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:3dcd802a5303d051a66c19658a279d8541b2b078020e353766309f9a860da23b5b067a06f970c45308d7b33501dbb9c2bbbcb78212683e80e46ac7381238e145
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73316/dot_73316_DS1.pdf
• File Type:
  [PDF - 3.56 MB ]