Disaggregate Mode Choice Analysis for Work Trips using Genetic-Fuzzy and Neuro-Fuzzy Systems

Y. Shafahi and S. Nazari (Iran)


Artificial Intelligence, Neural Networks, Mode Choice Analysis, Urban Transportation Demand Analysis


This paper applies the genetic-fuzzy and neuro-fuzzy systems to analyze a disaggregate mode choice problem for work trips. These two artificial intelligence methods have been widely used in many complex engineering problems recently. Some key points in developing genetic-fuzzy and neuro-fuzzy systems to analyze the mode choice problem for a large city are explained. Shiraz urban trips database is applied to prepare the data needed for the models. Results show that the developed models have good ability to estimate the existing situation and also to present reliable predictions for the future conditions of an urban transportation system.

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