The ability to collect or estimate accurate speed information is of great importance to a large number of Intelligent Transportation Systems (ITS) applications. Estimating speeds from the widely used single inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on empirical evidence observed from the sensor data from two metropolitan regions in Northern Virginia and California, this research effort developed a Kalman filter model to perform speed estimation for congested traffic. Taking advantage of the coexistence of dual loop and single loop stations in typical freeway management systems, a calibration procedure was proposed for seeding and initiating the algorithm. Empirical evaluation showed that the proposed algorithm can produce accurate speed estimates (on the order of 1-3 miles/hour error) under congested traffic conditions.


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    Title :

    Investigation of Speed Estimation Using Single Loop Detectors


    Contributors:
    J. Guo (author) / J. Xia (author) / B. Smith (author)

    Publication date :

    2008


    Size :

    21 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




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