Fuzzy Logic–Based Travel Demand Model to Simulate Public Transport Policies

Abstract

Four-stage travel demand modeling comprises of trip generation, trip distribution, mode choice, and traffic assignment. Limitations of conventional four-stage models are that they do not take into account subjectivity, imprecision, ambiguity, and vagueness involved in human decisions. In this direction, fuzzy logic is found to be the most suitable technique because it considers linguistic variables and expressions. Keeping this in view, the present study proposes to develop a methodology to consider fuzzy logic technique at different stages to develop travel demand models. The fuzzy logic–based travel demand models are developed in MATLAB software considering subtractive clustering technique. Four-stage conventional models are also developed to compare the efficiency of fuzzy logic models. The modeling results in terms R2, root-mean-square error (RMSE), and average error from both conventional and fuzzy logic models show that fuzzy logic models yield improved results in comparison to the conventional models. Further, to demonstrate the suitability of the developed fuzzy logic travel demand model, selected public transport policies are simulated considering appropriate parameters.

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