A Fuzzy and a Monte Carlo simulation approach to assess sustainability and rank vehicles in urban environment

Abstract

The selection and assessment of new vehicle technologies and mobility solutions to stimulate sustainable transportation planning becomes challenging, due to lack of frameworks and tools that are capable of considering the features of new solutions. The objective of this paper is to present a method that combines Fuzzy Logic and Monte Carlo Simulation (MCS) for the sustainability assessment of urban transportation vehicles and evaluate the applicability of the method to selected indicators for ranking the sustainability performance of vehicles. The fuzzy method has been chosen for its ability to incorporate imprecise and vague information in a decision-making process and the MCS for its ability to generate many scenarios by considering the random sampling of each probability distribution of uncertain input values. The results revealed that by using the fuzzy method alone or with MCS provide similar rankings. The MCS added value to the sustainability assessment by presenting the distribution characteristics of each sustainability index for the five vehicle types and added a layer of statistical confidence in interpreting and comparing the advantages and disadvantages between vehicle types.