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:
  • Vehicle classification
  • Traffic surveillance
  • Detection and identification system applications
  • Portable equipment
  • Radar
  • Vehicle classification
  • Highway traffic monitoring
  • Portable nonintrusive vehicle detection
  • LIDAR
  • Performance evaluation
• 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.
  More +
• Format:
  PDF
• Funding:
  State job no. 134516
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24434/dot_24434_DS1.pdf
• File Type:
  [PDF - 522.20 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:155be235bcc05ffcff6e60af74ba3b621b2b62b7928da252ca4534aab825db91c0023e735ca6450f1c4920addc10d941b28cd6d716dc9362c244d850ee9e871a