Estimating Truck Traffic Speed from Single-Loop Detector Data

Estimating truck traffic speed is a necessary step for constructing greenhouse gas emission heavy-duty trucks inventories. Embedded inductive loop detectors are the most prevalent infrastructure component for traffic monitoring in the U.S. In this paper, an algorithm for real time estimation of truck traffic speed from single-loop detector data is proposed. Conventional estimation algorithms (e.g. traffic speed estimation, truck classification, and truck traffic volume estimation) typically have their poorest performance during congestion compared to free flow conditions. Therefore, in this paper, we only focus on congested traffic by using a template matching algorithm to address the fluctuation of individual vehicle speeds during congestion. Experiments are done using freeway traffic data from the Berkeley Highway Laboratory, and the results verify the effectiveness of the proposed algorithm. An average absolute error of 3.5 mph to 5 mph during congestion of the truck traffic speed estimation is observed. The proposed template matching algorithm also has a potential application for vehicle classification.

Language

  • English

Media Info

  • Media Type: DVD
  • Features: Figures; References; Tables;
  • Pagination: 16p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01155569
  • Record Type: Publication
  • Report/Paper Numbers: 10-0744
  • Files: BTRIS, TRIS, TRB
  • Created Date: Jan 25 2010 10:21AM