Investigation of Truck Data Collection using LiDAR Sensing Technology along Rural Highways

Heavy trucks comprise a much larger proportion of overall traffic in rural highways compared with their urban counterparts, hence detailed classification counts are needed to adequately assess the impacts of truck activity in these regions. Under current practice, truck count data obtained along non-detectorized rural highway corridors are either estimated using unreliable growth factors applied on decades-old observed data or collected via pneumatic tubes which need to be laid across highways to collect traffic data. This study investigated a new truck classification approach using a Light Detection and Ranging (LiDAR) sensor array in a horizontal orientation, utilizing a reconstruction procedure that combines individual LiDAR frames with sparse point clouds to generate a feature-rich dense point cloud representation of vehicle objects to facilitate accurate truck classification. Two LiDAR-based classification models were developed in this study: an axle-based model following the FHWA-CA scheme and a detailed body classification model. The axle-based model demonstrated the ability to distinguish vehicle classes according to the FHWA-CA scheme on a truck-focused dataset with a correct classification rate (CCR) of 0.79, averaged across all classes. The corresponding CCR for the body classification model was 0.88 across 31 body classes. A preliminary investigation of LiDAR intensity on trailer surfaces was also performed to evaluate the potential of identifying fleet characteristics of trucks.

• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/67947
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Pacific Southwest Region University Transportation Center

  University of Southern California
  Los Angeles, CA  United States  90089

  University of California, Irvine

  Institute of Transportation Studies
  4000 Anteater Instruction and Research Building
  Irvine, CA  United States  92697

  Office of the Assistant Secretary for Research and Technology

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

  California Department of Transportation

  Sacramento, CA  United States  95819
• Authors:
  • Ritchie, Stephen G
  • 0000-0001-7881-0415
  • Tok, Andre
  • Li, Yiqiao
  • Sun, Jared
  • Allu, Koti R
• Publication Date: 2021-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; Photos; References; Tables;
• Pagination: 70p

Subject/Index Terms

• TRT Terms: Automatic vehicle classification; Data collection; Laser radar; Rural highways; Traffic counts; Trucks
• Subject Areas: Data and Information Technology; Highways; Motor Carriers; Vehicles and Equipment;

Filing Info

• Accession Number: 01887569
• Record Type: Publication
• Report/Paper Numbers: PSR-19-51
• Contract Numbers: 65A0674 TO 036
• Files: UTC, NTL, TRIS, ATRI, USDOT, STATEDOT
• Created Date: Jul 17 2023 9:13AM