Abstract Freeway travel time prediction has become a focus of research in recent years. However, we must understand that most conventional methods are very instinctive. They rely on the small amount of real-time data from the day of travel to look for historical data with similar characteristics and then use the similar data to make predictions. This approach is only applicable for a single day and cannot be used to predict the travel time on a day in the future (such as looking up the travel time for the coming Sunday on a Monday). This study therefore developed a Hammerstein recurrent neural network based on genetic algorithms that learns the freeway travel time for different dates. The trained model can then be used to predict freeway travel time for a future date. The experiment results demonstrated the validity of the proposed approach.


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    Titel :

    Freeway Travel Time Prediction by Using the GA-Based Hammerstein Recurrent Neural Network


    Beteiligte:
    Lee, Ru-Kam (Autor:in) / Yang, Yi-Che (Autor:in) / Chen, Jun-Hong (Autor:in) / Chen, Yi-Chung (Autor:in)


    Erscheinungsdatum :

    18.10.2017


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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