The paper discusses the calibration methodology of a neuro-fuzzy logic for route choice behaviour modelling. Neuro-fuzzy refers to the trend of logics that couple the traditional fuzzy logic structure with neural nets training capabilities for knowledge base and parameters settings. The fuzzy logic accounts for the various factors of potential effect on the route choice utility perceived by the traveller. The structure of the fuzzy logic, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Developing fuzzy route choice models using neural nets


    Contributors:
    Hawas, Y.E. (author)

    Published in:

    Publication date :

    2002-01-01


    Size :

    425890 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Developing Fuzzy Route Choice Models Using Neural Nets

    Hawas, Y. E. / INRIA / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2003


    Route Choice Models

    Ben-Akiva, M. E. / Ramming, M. S. / Bekhor, S. | British Library Conference Proceedings | 2004


    Random and fuzzy utility models for road route choice

    Quattrone, Agata | Online Contents | 2011


    ROUTE CHOICE BEHAVIOR MODEL USING FUZZY INFERENCE

    Lee, B. / Fujiwara, A. / Namgung, M. et al. | British Library Conference Proceedings | 2003