The current signal control systems are limited by the data acquisition of traditional sensors, which can only acquire limited vehicle information. However, connected vehicles in intelligent transportation systems can provide richer vehicle information, which provides opportunities to improve traffic flow and reduce delays at intersections. This paper proposes a new traffic signal control algorithm called the predictive numerical simulation algorithm (PNSA), which combines vehicles’ positions, headings, accelerations, and speeds information from connected vehicles with the modified car-following model to determine phasing and timings. The PNSA has the ability to respond to various traffic volumes without retiming automatically. The PNSA is compared with the actuated signal control algorithm and an adaptive signal control algorithm proposed by the previous study on a single intersection. The results show that the PNSA offers reductions in average delay and average numbers of stops of up to 38% and 22% respectively over the actuated signal control algorithm, and reductions in average delay and average numbers of stops of up to 65% and 65% respectively over the previous algorithm, for networks with 25–100% connected vehicle presence. Furthermore, the PNSA maintains or improves performance compared to the other algorithms under various traffic low levels with the high level of CV penetration, while performance becomes worse during saturated and oversaturated conditions with low levels of CV penetration.


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    Title :

    Traffic Signal Control for Urban Intersection Under Connected Vehicle Data Environment


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Zhang, Zhenyuan (editor) / Jiao, Yubo (author) / Liu, Kun (author) / Chen, Yuhong (author) / Zhang, Xiaoming (author) / Jiang, Chaozhe (author) / Zhang, Shuai (author)

    Conference:

    International Conference on Intelligent Transportation Engineering ; 2021 ; Beijing, China October 29, 2021 - October 31, 2021



    Publication date :

    2022-06-01


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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