Abstract
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 language | English |
---|---|
Pages (from-to) | 171-182 |
Number of pages | 12 |
Journal | Journal of Transportation Engineering |
Volume | 130 |
Issue number | 2 |
DOIs | |
Publication status | Published - Mar 2004 |
Keywords
- Calibration
- Fuzzy sets
- Neural networks
- Questionnaires
- Route preferences
- State-of-the-art reviews
- Validation
ASJC Scopus subject areas
- Civil and Structural Engineering
- Transportation