Use of Artificial Intelligence for Mode Choice Analysis and Comparison with Traditional Multinomial Logit Model

Travel Demand Forecasting, an essential tool to predict the future demand, is a four stage procedure which involves trip generation, trip distribution, mode choice and traffic assignment, out of which, mode choice analysis plays vital role as it deals with predicting mode used by the travelers to reach their destination. Multinomial Logit (MNL) model is a traditional model adopted for mode choice analysis which has major limitation that the input variables need to have crisp values and hence should be measured accurately which consumes lot of time and resources. Moreover, decision of trip maker for choosing a mode involves human approximations which are not precisely captured by MNL model. This can be overcome by using artificial intelligence techniques like fuzzy logic for modeling mode choice behavior. Fuzzy logic try to harness the human knowledge which is often guided by approximations by accepting input values in linguistic terms. The fuzzy rule base comprises several IF-THEN rules which closely resemble human knowledge and decision-making. In this study, it was thus proposed to apply the concept of fuzzy logic for modeling mode choice and compare the results with traditional MNL model. For this purpose, a total of 5822 samples were collected in Port Blair city, India and data pertaining to input variables viz. in-vehicle travel time, out-vehicle travel time, travel cost and comfort index were considered for development of mode choice models. It was observed that the results obtained from fuzzy logic results gave better prediction accuracy in comparison to the traditional MNL model. Thus it can be concluded that the fuzzy logic models were better able to capture and incorporate the human knowledge and reasoning into mode choice behaviour. Further, developed fuzzy logic models are applied to evaluate selected transport policies to demonstrate the suitability of the developed fuzzy logic mode choice models. (C) 2013 The Authors. Published by Elsevier Ltd.