As the complexity of traffic situation in each intersection is different, the applicability of the evaluation model established through one or more fixed parameters to all intersections will be greatly affected. Effectively assessing the congestion state of intersections to guide the optimization of the signal light timing plan has become one of the important methods to solve the traffic congestion problem. First, this paper establishes a set of ten traffic flow parameters, including capacity, average delay, parking rate, and obtains a set of sample data sets containing these ten parameters every 10 min through mathematical models and simulations. Next, the K-Means algorithm is used to cluster all the samples multiple times, and the mutual information method is used to filter the correlation between each parameter and the clustering result label. The set of traffic flow parameters with high correlation with the state is obtained by sorting the correlation degree, and data normalization is performed on them. Finally, a tower structure is used to comprehensively evaluate the multi-influencing factors at the intersection, and the traffic states and the thresholds of each state are determined through K-Means clustering method. The results of the simulation experiment show that the dynamic evaluation model proposed in this paper can more accurately reflect the changes of the traffic state.


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    Titel :

    Dynamic Evaluation Model of Traffic Congestion State at Urban Intersections


    Beteiligte:
    Deng, Qingwei (Autor:in) / Zhang, Weibin (Autor:in) / Li, Xiying (Autor:in)

    Kongress:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Erscheinungsdatum :

    31.08.2022




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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