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

Filetype[PDF-522.20 KB]


English
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  • Creators:
  • Corporate Creators:
    Ohio State University
  • Subject/TRT Terms:
  • 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
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