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, 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.


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    Titel :

    Validating the Performance of Vehicle Classification Stations, Executive Summary


    Erscheinungsdatum :

    2012


    Format / Umfang :

    4 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch




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