Development and calibration of route choice utility models: Neuro-fuzzy approach

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)


The neuro-fuzzy refers to the recent technology that couples the traditional fuzzy logic developments with neural nets training capabilities to compose the fuzzy logic's knowledge base and fuzzy sets' parameters optimally. This paper discusses the calibration methodology of a neuro-fuzzy logic for modeling the route choice behavior. The logic accounts for the various factors of potential effect on the route choice utility perceived by the traveler. The structure of the fuzzy control stages, 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. The results of the fuzzy logic model are utilized for in-depth analyses of the travelers' perceptions of the route utility in response to the various traffic states.

Original languageEnglish
Pages (from-to)171-182
Number of pages12
JournalJournal of Transportation Engineering
Issue number2
Publication statusPublished - Mar 2004


  • Calibration
  • Fuzzy sets
  • Neural networks
  • Questionnaires
  • Route preferences
  • State-of-the-art reviews
  • Validation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation


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