This chapter proposes a novel approach, based on fuzzy logic and genetic algorithms, to build traffic congestion prediction systems from a high number of input variables. For the purpose of achieving more accurate and robust traffic prediction, it proposes a genetic hierarchical fuzzy rule‐based system (GHFRBS) capable of predicting traffic congestion in multiple prediction horizons. The chapter first gives preliminary concepts of HFRBS, and then introduces the implementation of GHFRBS. To evaluate the feasibility of the proposed method, namely, GHFRBS, an experimental study was carried out. The chapter presents the experimental setup, including its datasets, techniques and parameters, elaborates the obtained results, and analyzes the obtained results in details, including the accuracy and complexity of the obtained models.


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    Titel :

    Congestion Prediction by Means of Fuzzy Logic and Genetic Algorithms


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    05.11.2015


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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