An unconventional left-turn treatment called contraflow left-turn lane (CLL) design has been increasingly used in China to relieve traffic congestion associated with left-turn movements at signalized intersections. This study proposed a procedure for estimating the left-turn queue length at signalized intersections with the CLL design. Field data were collected at six approaches at five signalized intersections in the city of Handan, China, and 40 h of traffic data were recorded. A binary logit model was developed to estimate the probability of a driver stopping at the presignal when there are still vacant spaces in the conventional left-turn lane between the main and presignals. A modified shockwave-based method was then proposed for estimating the maximum left-turn queue length for the signalized intersections with the CLL design by considering the unique queuing behavior at the presignal. Sensitivity analyses were conducted to identify the impacts of the key parameters in the proposed queue length model on the maximum left-turn queue length. The results suggest that the effective red time duration, the offset between the main and presignals, and the queuing behavior of left-turning vehicles at the presignals greatly affect the maximum left-turn queue length. The proposed queue-length model was validated with the field data considering varying left-turn traffic demands. The results indicated that the proposed model provides reasonable estimates for the maximum left-turn queue length at the signalized intersections with the CLL design. A procedure was also proposed to help determine the minimum distance between the upstream and downstream signalized intersections for the deployment of the CLL design.


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    Title :

    Estimating Queue Length for Contraflow Left-Turn Lane Design at Signalized Intersections


    Contributors:
    Liu, Pan (author) / Wu, Jiaming (author) / Zhou, Huaguo (author) / Bao, Jie (author) / Yang, Zhao (author)


    Publication date :

    2019-03-20




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






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