Real-time queue length information at signalized intersections is useful for both performance evaluation and signal optimization. Previous studies have successfully examined the use of high-resolution event-based data to estimate real-time queue lengths. Based on the identification of critical breakpoints, real-time queue lengths can be estimated by applying the commonly used shockwave model. Although breakpoints can be accurately identified using lane-by-lane detection, few studies have investigated queue length estimation using single-channel detection, which is a common detection scheme for actuated signal control. In this study, a breakpoint misidentification checking process and two input-output models (upstream-based and local-based) are proposed to address the overestimation and short queue length estimation problems of breakpoint-based models. These procedures are integrated with a typical breakpoint-based model framework and queue-over-detector identification process. The proposed framework was evaluated using field-collected event-based data along Speedway Boulevard in Tucson, Arizona. Significant improvements in maximum queue length estimates were achieved using the proposed method compared to the breakpoint-based model, with mean absolute errors of 35.7 and 105.6 ft., respectively.


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    Title :

    Real-time queue length estimation using event-based advance detector data


    Contributors:
    An, Chengchuan (author) / Wu, Yao-Jan (author) / Xia, Jingxin (author) / Huang, Wei (author)

    Published in:

    Publication date :

    2018-07-04


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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