Citizen-based decision support for sustainable public transport system selection: Evidence from Vietnam

Over the last decade, Multi-Criteria Decision-Making Methods (MCDMMs) have been widely applied in public transport to support alternative selection. However, despite citizens being primary users, their opinions are rarely considered in choosing transit modes. Moreover, the variety of MCDMMs can make method selection challenging, and, to the best of the authors' knowledge, no prior research in transportation has quantitatively compared multiple MCDMMs under the noise effect, an essential factor for assessing decision robustness in participatory contexts.This study addresses these gaps by proposing an integrated multi-method decision framework combining Analytic Hierarchy Process (AHP) for citizen-based weighting with Simple Additive Weighting (SAW), Techniques of Order of Preference by Similarity to Ideal Solution (TOPSIS), VIekriterijumsko KOmpromisno Rangiranje (VIKOR), and Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE II) for alternative ranking. In addition, two quantitative stability metrics—score-based and ranking-based—are introduced to assess methodological robustness under stochastic weight perturbations generated through Monte Carlo simulations.The framework reduces single-method bias by integrating multiple MCDMMs, while noise analysis ensures reliability under data uncertainty, particularly when citizen-based inputs inform urban mobility strategies. Its viability is tested in Ho Chi Minh City in Vietnam, a rapidly urbanizing megacity under pressure to promote sustainable mobility choices. Results show that assessing both metrics offers a fuller picture of robustness, as methods may be stable in scores or unstable in rankings.The consistent patterns observed in the study support the methodological transferability of the framework, offering a structured benchmark for planners, practitioners, and academics assessing public transport system alternatives in comparable urban contexts.