Analyzing Public Travel Demand by a Fuzzy Analytic Hierarchy Process Model for Supporting Transport Planning

Abstract

Travel demand plays an essential role in strategic transport planning. Generally, experts use either discrete methods, e.g. discrete choice models or simulation, e.g. activity-based models to estimate demand in transportation. This paper offers a different solution; instead of using the traditional approach, the demand is considered as a Multi Criteria Decision Making (MCDM) problem and surveying the citizens’ preferences provides the results for decision support. Public transport demand depends on two main issues, quality and price of the transportation. In a hierarchical model, both issues have been integrated and the well-proven Analytic Hierarchical Process (AHP) method has been applied in the current research. Further, fuzzyfication of the scores have also been conducted because of the citizen evaluator pattern. The fuzzy-AHP (FAHP) model has been tested in a real-world situation with the case study of Amman (Jordan).

McDm Approaches for Evaluating Urban and Public Transportation Systems: A Short Review of Recent Studies

“…To deal with P&R problem, Barauskas et al (2018) also proposed a methodology based on the EDAS method. Alkharabsheh et al (2022) analysed travel demand based on a real-world problem in Amman (Jordan). According to their study, the transport quality issues needed to be improved with a focus on the safety of traveling by public vehicles.…”

An Integrated Decision Support Model for effective Institutional Coordination Framework in Public Transportation Planning

“…Finding the appropriate strategy to resolve these issues in public transportation planning and achieve effective coordination is a complicated task that demands the expertise of a multidisciplinary team. Multi-Criteria Decision Making (MCDM) is an effective and adaptable approach for tackling public transportation planning issues (Alkharabsheh et al, 2022;Bouraima et al, 2022;Bouraima et al, 2020;Bouraima et al, 2021;Bouraima et al, 2023). The decision-making procedure often presents challenges to experts due to incomplete information and uncertainty.…”