A methodological framework for a priori selection of travel demand management package using fuzzy MCDM methods

A robust and scientific selection of appropriate Travel Demand Management (TDM) measures is likely to ensure that the purpose of their implementation is met. The existing methods of TDM measure selection do not often adopt a multi-dimensional approach in their decision-making process. Besides, there is minimal scholarly work on the selection of a ‘push plus pull’ integrated TDM package, even though it has been emphasized in the published literature. These gaps are addressed in our study, which adopts a novel approach of a Multi-Criteria Decision Making (MCDM) methodology. A combination of ‘Decision Making Trial and Evaluation Laboratory’ (DEMATEL) and ‘Analytical Network Process‘(ANP), along with the VIKOR, which is a ranking method used to select a suitable TDM package, a priori. The demonstration of the proposed methodology is presented for the case study area of Ahmedabad’s old city, which is located in the western state of Gujarat in India. A total of six TDM packages were initially shortlisted as probable contenders for the study area, each package consisting of one coercive (push) and one attracting (pull) measure. The MCDM tool ‘VIKOR’ was used to derive the ranking of these packages based on their performance in seven different criteria. The DEMATEL based ANP method was used to determine the weights of these criteria by comparing the influence of criteria over each other. The uncertainty and subjectivity involved in the entire process was taken into account by employing fuzzy logic into our proposed methodology. The research contribution of the study is a methodological framework that can assist policymakers in the a priori selection of a TDM package. The outputs from the study are twofold—(a) the Influence Network Relationship Map (INRM) and (b) the ranking of the TDM packages for the case study area. The INRM explains the relations between dimensions as well as between criteria within each dimension. Additionally, it classifies the dimension and criteria into cause and effect groups, which can help policymakers in developing a better understanding of the TDM implementation process. In our case study, the TDM package of congestion pricing and public bike sharing was the top ranked package. The most influential dimension was political feasibility, followed by private participation and public acceptability. In addition, effectiveness, investment attractiveness, and system resistance were the most influencing criteria within their respective dimensions.