Validating the performance of vehicle classification stations : executive summary report.

Details

• Creators:
  Coifman, Benjamin ; Lee, Ho ; Kim, Seoungbum
• Corporate Creators:
  Ohio State University
• Subject/TRT Terms:
  Detection And Identification System Applications Highway Traffic Monitoring LIDAR Performance Evaluation Portable Equipment Portable Nonintrusive Vehicle Detection Radar Traffic Surveillance Vehicle Classification
• Publication/ Report Number:
  FHWA/OH-2012/7
• Resource Type:
  Tech Report
• Geographical Coverage:
  Ohio
• Edition:
  Executive summary report.
• Corporate Publisher:
  Ohio. Dept. of Transportation
• Abstract:
  Vehicle classification data are used in many transportation applications, including: pavement design,
  
  environmental impact studies, traffic control, and traffic safety. Typical of most developed countries, every
  
  state in the US maintains a network of vehicle classification stations to explicitly sort vehicles into several
  
  classes based on observable features, e.g., length, number of axles, axle spacing, etc. Various
  
  technologies are used for this automated classification, the three most common approaches are: weigh in
  
  motion (WIM); axle-based classification from a combination of loop detectors, piezoelectric sensors or
  
  pneumatic sensors; and length-based classification from dual loop detectors. Each sensor technology has
  
  its own strengths and weaknesses regarding costs, accuracy, performance, and ease of use.
  
  As noted in the Traffic Monitoring Guide [1], the quality of data collected depends on the operating
  
  agency to periodically calibrate, test, and validate the performance of classification sensors. However,
  
  such a periodic performance monitoring has been prohibitively labor intensive because the only option
  
  has been to manually validate the performance, e.g., classifying a sample by hand. Furthermore, the
  
  manual classifications are prone to human error and conventional aggregation periods allow classification
  
  errors to cancel one another.
  
  To address these challenges, this study examined three interrelated facets of vehicle classification
  
  and classification performance monitoring. First, we manually evaluate the performance of vehicle
  
  classification station on a per-vehicle basis, second we develop a portable LIDAR (light detection and
  
  ranging) based vehicle classification system that can be rapidly deployed, and third we use the LIDAR
  
  based system to automate the manual validation done in the first part using the tools from the second
  
  part.
  
  
• Format:
  PDF
• Funding:
  State job no. 134516
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:921dcc1859b0697be31d46e5010cef547e05da0c62b301fbfa19726fbe4ac8a0
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24434/dot_24434_DS1.pdf
• File Type:
  [PDF - 522.20 KB ]