Fuzzy Logic Approach in Mode Choice Modelling for Education Trips: A Case Study of Indian Metropolitan City

Abstract

Urban population in India has increased significantly from 62 million in 1951 to 378 million in 2011 in six decades. It is estimated to reach 540 million by the year 2021. This reflects on likely pressure on urban transportation system. The situation necessarily calls plans for balanced personal and public transport system. Mandatory trips bear more importance in this regard owing to their higher share in urban trips. Mode share and their choice behaviour in estimation of such trips play vital role in analysing and boosting sustainable transportation. Logit modelling approach is the conventional method generally adopted for analysing mode choice behaviour, which is based on the principle of random utility maximization derived from econometric theory. However, such models cannot address uncertainity prevailing in the choice decisions. On the contrary, fuzzy logic bypasses the binary crisp derivations of the inputs and accepts multivalued inputs in linguistic expressions, which make possible to resemble the human behaviour closely. Therefore, the attempt here is to develop fuzzy logic based mode choice model for education trips, which constitutes a good share in mandatory trips by covering various income groups of Indian society.

Multi-route choice modelling in a metropolitan context: A comparative analysis using Multinomial Logit and Fuzzy Logic based approaches

“…However, triangular MFs have been used in several studies (e.g. Dhulipala et al, 2017;Kedia et al, 2015Kedia et al, , 2017 1994) and were found to be promising. Thus, triangular MFs are adopted to fuzzify the crisp inputs with five fuzzy sets for both input and output variables.…”

“…Centroid), Centre of area, Maximum height, and Means of maxima. However, the most commonly used method is the Centroid method, because it considers the weighted average of the elements in the support set and analyses the combined shape of membership functions, which results in better results (Kedia et al, 2015;Kumar et al, 2013).…”

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

“…Later, researchers have incorporated AI techniques such as ANN [17][18][19], FL [20][21][22], NF [23][24][25], support vector machine and random forest decision tree [26] to develop mode-choice models. The use of AI techniques in travel demand modelling began in 1960.…”

“…The obtained results led to improved accuracy compared with the traditional MNL model indicating that FL models were better able to capture and incorporate the human knowledge and reasoning into mode-choice behaviour. Kedia et al [22] modelled the travel mode choice for educational trips for the city of Surat, India using FL theory. The results analysed mode usage pattern for different income groups (low, medium and high).…”

Towards a Fair and More Transparent Rule-Based Valuation of Travel Time Savings

“…A review of the applications of fuzzy logic in transport can be found in [18]. In contrast to previous studies, this paper does not focus on using fuzzy logic for choice modelling (route choice, mode choice) [19][20][21][22] but rather it proposes to use a fuzzy inference system (FIS) to directly estimate VOTTS of an individual, country (or at least of a group of people).…”