Developing fuzzy route choice models using neural nets

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.