Intelligent transportation systems (ITS) has become a vital means of traffic management and control tool in the entire world. It provides necessary subsystems and functions to classify the traffic state. This paper proposed a dynamic automatic condition identification based local accuracy (DACI-LA) algorithm to classify urban road traffic state level. The new useful information traffic state was obtained from the DACI-LA. The learning ability of integrated classifiers can solve the limitation that pure classification methods can’t train sample data. Finally, the experiment results indicated that ensemble learning method can reduce the influence of noise sample data and provide better identification accuracy.


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    Titel :

    Urban Road Traffic State Level Classification Based on Dynamic Ensemble Learning Algorithm


    Beteiligte:
    Liu, Qingchao (Autor:in) / Cai, Yingfeng (Autor:in) / Jiang, Haobin (Autor:in) / He, Youguo (Autor:in) / Chen, Long (Autor:in)

    Kongress:

    17th COTA International Conference of Transportation Professionals ; 2017 ; Shanghai, China


    Erschienen in:

    CICTP 2017 ; 823-830


    Erscheinungsdatum :

    18.01.2018




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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