Development of neuro-fuzzy-based multimodal mode choice model for commuter in Delhi

Delhi is highly plagued by traffic congestion and is notoriously known for its traffic jams. Thus, the question of studying the mode-choice preferences of commuters in Delhi will be integral to travel demand forecasting. The study area poses a challenge in terms of heterogeneity in different types of travel modes available as well as commuters with heterogeneous backgrounds. It offers the typical mix traffic situation prevalent in developing countries, which is cumbersome to model. Eight modes of travel have been considered in this study, which is difficult to come across in previous studies found in the literature. This study proposes to capture mode-choice preferences of commuters by using an adaptive-neuro-fuzzy classifier (ANFC) with linguistic hedges. The proposed mode-choice model will have improved 'distinguish-ability' in terms of less overlapping amongst classes, so that the prediction ability is highly improved. Artificial neural network, fuzzy-logic and multinomial-logit models have also been used for analysing mode-choice behaviour of commuters in Delhi. This study is based on microdata collected through household survey conducted in the study area. Results depict that mode-choice model developed by ANFC performs superior to the other models in terms of prediction accuracy.