Exploratory methods for truck re-identification in a statewide network based on axle weight and axle spacing data to enhance freight metrics : phase II.

Details

• Creators:
  Cetin, Mecit ; Monsere, Christopher M.
• Corporate Creators:
  Portland State University ; Old Dominion University. Dept of Civil and Environmental Engineering
• Corporate Contributors:
  United States. Department of Transportation. Research and Innovative Technology Administration ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Trucks
  • Trucks by number of axles
  • Trucks by weight
  • Trailers
  • Policy
  • Standards
  • Identification systems
  • Trucks--Weight--Government policy--Oregon
  • Truck trailers--Standards--Oregon
• Publication/ Report Number:
  OTREC-RR-12-04
• Resource Type:
  Tech Report
• Geographical Coverage:
  Oregon
• Edition:
  Final report.
• Corporate Publisher:
  Oregon Transportation Research and Education Consortium
• Abstract:
  Vehicle re-identification methods can be used to anonymously match vehicles crossing two different locations based on vehicle attribute data. ; This research builds upon a previous study and investigates different methods for solving the re-identification problem and explores some of the ; factors that impact the accuracy of the results. To support this work, archived data from weigh-in-motion (WIM) stations in Oregon are used for ; developing, calibrating, and testing vehicle re-identification algorithms. In addition to the Bayesian approach developed by the researchers in ; the previous study, a neural network model is developed for solving the re-identification problem. The results from the testing datasets showed ; that both methods can be effective in solving the re-identification problem while the Bayesian method yields more accurate results. A ; comprehensive analysis is performed to investigate the key factors impacting the accuracy of the results. The analyses are performed by ; employing the Bayesian algorithm to match commercial vehicles that cross upstream and downstream pairs of WIM sites that are separated by ; long distances ranging from 70 to 214 miles. Data from 14 different pairs of WIM sites are used to evaluate how matching accuracy is impacted ; by various factors such as the distance between two sites, travel time variability, truck volumes, and sensor accuracy or consistency of ; measurements. After running the vehicle re-identification algorithm for each one of these 14 pairs of sites, the matching error rates are reported. ; The results from the testing datasets showed a large variation in terms of accuracy. It is found that sensor accuracy and volumes have the ; greatest impacts on matching accuracy whereas the distance alone does not have a significant impact. Overall, for estimating travel times and ; origin-destination flows between two WIM sites, the methods developed in this project can be used to effectively match commercial vehicles ; crossing two data collection sites that are separated by long distances.
  More +
• Format:
  PDF
• Funding:
  2010-327
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24292/dot_24292_DS1.pdf
• File Type:
  [PDF - 1.52 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:435f5348ab65a23db63b901c2441651f4a05be4f5f99b7a28175ae9cdaafb64228a91a1a912878bb5c3e0b52bd39675afb666c55026e4572fba4194f03953913