Freeway Travel Time Estimation using Existing Fixed Traffic Sensors – A Computer-Vision-Based Vehicle Matching Approach

Vehicle re-identification is investigated as a method to analyze traffic systems, such as the estimation of travel time distribution in a freeway network. In this paper, a vision-based algorithm is proposed to match vehicles between upstream and downstream videos captured by low-resolution (360*240) surveillance cameras and then estimate the travel time distributions. The algorithm consists of three stages: (1) vehicles are detected by Motion History Image (MHI) and Viola-Jones vehicle detector, and then image segmentation and warping are conducted to the detected vehicle images; (2) features (e.g., size, color, texture) are extracted from vehicle images to uniquely describe each vehicle in low-resolution images; and (3) vehicles from two cameras are matched by solving two problems: a Support Vector Machine (SVM) classifies whether a pair of vehicles are identical or not, and linear programming globally matches groups of vehicles between upstream and downstream cameras with context constraints. The proposed algorithm was validated on two sections of freeway in St. Louis, Missouri, United States, which outperforms the state-of-the-art methods and accurate travel time estimation is achieved based on the re-identification results.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Photos; References; Tables;
  • Pagination: 39p

Subject/Index Terms

Filing Info

  • Accession Number: 01675635
  • Record Type: Publication
  • Report/Paper Numbers: MATC-MS&T: 296, 25-1121-0003-296
  • Files: UTC, TRIS, ATRI, USDOT
  • Created Date: Jul 10 2018 12:12PM