Developing fuzzy route choice models using neural nets

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationIEEE Intelligent Vehicles Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0780373464
Publication statusPublished - 2003
Event2002 IEEE Intelligent Vehicle Symposium, IV 2002 - Versailles, France
Duration: Jun 17 2002Jun 21 2002

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


Other2002 IEEE Intelligent Vehicle Symposium, IV 2002

ASJC Scopus subject areas

  • Modelling and Simulation
  • Automotive Engineering
  • Computer Science Applications


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