Developmental analysis on mode Choice behaviour of commuters by Multinomial logit model with Hybridizing fuzzy neural network

Abstract

Based on the four-step transportation forecasting model, such as trip generation, mode choice analysis, trip distribution and route assignment, mode choice analysis plays an outstanding value in transportation analysis. Nowadays it could be found that it increases daily and usage of public transport systems decreases than private vehicle systems. In this context, mode choice analysis plays a significant role in transportation decisions by Multinomial Logit Modelling (MLM). This research describes the results of predicting the decision-making behaviour of commuters while considering the characteristics of different modes of transport. It is an essential step towards chieving the objectives of sustainable transport, which include: the safety aspect of traffic efficiency, which in turn helps to reduce environmental constraints. In this scenario, this research work focused on the significant factors in the mode choice behaviour of commuters from the extensive household survey conducted by Thiruvarur City to obtain detailed information on the modal split pattern analysis. The questionnaire of 4857 respondents was used in the Multinomial Logit Model using the Statistical Packages for Social Science (SPSS) tool to analyze the mode choice behaviour of commuters. A total of seven explanatory variables, such as Age, Gender, Monthly Income, Distance, Time, Cost and Comfort, were extracted from the responses. The Multinomial logit model was calibrated to fit the data, and the chi-square test was used to measure the goodness of fit. From the analysis, about 61.7 percent of the respondents of the trip makers preferred the two-wheeler for comfort mode choice. newline