Transportation systems today continue to be designed and planned with an implicit assumption of a stable and predictable future. This conventional “predict-and-provide” approach does not account for the frequent and often unpredictable disruptions like extreme weather, pandemics, cultural events, or broader societal shifts that increasingly affect mobility. In direct alignment with the UN’s eleventh Sustainable Development Goal, transportation systems must integrate resilience as a fundamental component to maintain their functionality and sustain their core societal responsibility – generating social welfare – during disruptions. However, current definitions and operationalizations of resilience remain predominantly rooted either in engineering and technical perspectives or qualitative assessments in socio-ecological dimensions. This research contributes a novel perspective on transportation resilience by developing a methodology to quantify and monetize resilience in terms of social welfare. Mobility resilience captures the transportation system’s capacity to sustain societal welfare and environmental sustainability as a function of the operators’ costs and revenues, users’ utility of activities and travel, and social external costs. This methodology is implemented using agent-based modeling based on empirical data to evaluate the performance of private cars, public transportation, and shared autonomous vehicles during an extreme weather scenario, a large-scale cultural event, a public transportation labor strike, and a pandemic in Hamburg, Germany. These scenarios assess the different transportation systems with their unique characteristics regarding their capacity to improve social welfare, resilience, and recovery. By bridging technical robustness and socio-economic sustainability, this research’s contributions enable stakeholders to create resilient transportation systems that remain socially effective during disruptions. • Monetizing transportation systems’ resilience and welfare capabilities. • Integrate consumers, producers, and externalities in an agent-based MATSim model. • Compare Car, PT, and SAV Systems during disruptions scenarios.
Pfeil et al. (Thu,) studied this question.