Real-time speed and truck data are important inputs for modern freeway traffic control and management systems. However, these data are not directly measurable by single-loop detectors. Although dual-loop detectors provide speeds and classified vehicle volumes, there are too few of them on our current freeway systems to meet the practical needs of advanced traffic management systems. This makes it extremely desirable to develop appropriate algorithms to calculate speed and truck volume from single-loop outputs or from video data. To obtain quality estimates of traffic speed and truck volume data, several algorithms were developed and implemented in this study. These algorithms are (1) a speed estimation algorithm based one region growing mechanism and single-loop measurements; (2) a set of computer - vision-based algorithms for extracting background images from a video sequence, detecting the presence of vehicles, identifying and removing shadows, and calculating pixel-based vehicle lengths for classification; and (3) a speed estimation algorithm that uses paired video and single- loop sensor inputs. These algorithms were implemented in three distinct computer applications. Field-collected video and loop detector data were used to test the algorithms. Our test results indicated that quality speed and truck volume data can be estimated with the proposed algorithms by using single-loop data, video data, or both video and single-loop data.


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    Title :

    Improving Truck and Speed Data Using Paired Video and Single-Loop Sensors


    Contributors:
    Y. Wang (author) / N. Nihan (author) / R. P. Avery (author) / G. Zhang (author)

    Publication date :

    2006


    Size :

    128 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




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