A method for formulating and assessing public transport interventions based on equity principles

Abstract

Urban transport planning has traditionally prioritized efficiency over social equity, often reinforcing accessibility disparities. Conventional ex-ante evaluations, based on cost-benefit analysis, fail to account for the needs of vulnerable populations. This thesis develops a method for formulating public transport interventions based on equity principles, integrating strategic planning, equity-driven optimization, and resilience in ex-ante evaluation. The first contribution is a structured planning approach that directly links problem diagnosis to intervention formulation, ensuring that proposed projects are based on actual accessibility constraints rather than arbitrary assumptions. The second is an equity-driven optimization model that prioritizes accessibility improvements for the most disadvantaged populations, shifting transport planning from a purely utilitarian perspective to one based on social justice principles. The third contribution introduces resilience into ex-ante evaluations, allowing planners to assess how accessibility gains hold up under disruptions, ensuring long-term stability in equitable transport access. These contributions translate into three practical frameworks: a planning methodology for addressing accessibility inequities, an optimization tool for equitable transport intervention design, and a resilience informed evaluation method for stress-testing projects before implementation. The application of the proposed framework in the case study demonstrated its effectiveness in guiding equitable and resilient public transport investments. Results showed that interventions formulated using a problem-oriented approach significantly reduced accessibility gaps, particularly for vulnerable populations. The equity-driven optimization model successfully prioritized transit improvements that minimized the number of individuals below critical accessibility thresholds while maintaining operational feasibility. Additionally, resilience analysis revealed that might have a good performance on the expected scenario, but a distinct behavior when confronted by disruptions. These findings confirm that integrating diagnostic-based planning, capability-driven optimization, and resilience evaluation into transport decision-making leads to more just and sustainable urban mobility outcomes. Despite its strengths, the research is limited by assuming static land-use and travel demand conditions. Future research should incorporate behavioral modeling to account for demand-side responses, integrate land-use interactions to explore long-term accessibility changes, and further develop resilience-based multi-objective optimization techniques.