The effects of architecture, learning mode, and learning rate on the performance of a level-of-service (LOS) analysis model using an artificial neural network (ANN) are discussed. Multilayer LOSANN models demonstrated improved quality of learning and testing over single-layered models in evaluating level of service of signalized intersections given geometric, traffic, and traffic signal control data. At present, LOSANN takes delay data from Highway Capacity Software (HCS) outputs; hence its accuracy is constrained by the accuracy of the HCS analyses. However, if delays can be determined directly by field observation, the relationships (or patterns) between field-measured delays and the traffic, geometric, and signal control conditions can be fed to LOSANN. Then the neural network-based model can evaluate the level of service at a higher level of accuracy, and such models can be used as part of advanced traffic management systems to automate LOS analyses.


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    Titel :

    Multilayer Artificial Neural Networks for Level-of-Service Analysis of Signalized Intersections


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Saito, Mitsuru (Autor:in) / Fan, Jianzhong (Autor:in)


    Erscheinungsdatum :

    01.01.1999




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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