The traffic state identification algorithm based on a single detector data source is often unable to distinguish successfully due to the low prediction accuracy caused by too long distance of fixed monitors and the lack of data caused by communication failure. In recent years, under the background of abundant multi-source data, it has become an important method to use various traffic data information to improve traffic state identification accuracy. This paper proposes a traffic state identification algorithm based on multi-feature fuzzy state recognition method. The algorithm can give weight to traffic data from different sources according to detector types and detection accuracy of the road section to be detected, and identify the data comprehensively. It overcomes the shortcomings of traffic identification methods with single data source, which has low accuracy and is easy to make mistakes.


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    Title :

    Multi-source Data Congestion Recognition Study Based on Fuzzy Pattern Recognition


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wang, Wuhong (editor) / Chen, Yanyan (editor) / He, Zhengbing (editor) / Jiang, Xiaobei (editor) / Li, Junzhuo (author) / Li, Wenyong (author) / Guo, Bin (author)


    Publication date :

    2021-12-14


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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