A methodological framework for Resilience as a Service (RaaS) in multimodal urban transportation networks

Public transportation systems are experiencing an increase in commuter traffic. This increase underscores the need for resilience strategies to manage unexpected service disruptions, ensuring rapid and effective responses that minimize adverse effects on stakeholders and enhance the system’s ability to maintain essential functions and recover quickly. This study aims to explore the management of public transport disruptions through resilience as a service (RaaS) strategies, developing an optimization model to effectively allocate resources and minimize the cost for operators and passengers. The proposed model includes multiple transportation options, such as buses, taxis, and automated vans, and evaluates them as bridging alternatives to rail-disrupted services based on factors such as their availability, capacity, speed, and proximity to the disrupted station. This ensures that the most suitable vehicles are deployed to maintain service continuity. Applied to a case study in the Ile de France region (Paris and its suburbs), complemented by a microscopic simulation, the model is compared to existing solutions such as bus bridging and reserve fleets. The results highlight the model’s performance in minimizing costs and enhancing stakeholder satisfaction, optimizing transport management during disruptions. • Introduce a novel RaaS optimization model for managing urban transport disruptions. • Develop a dynamic model for vehicle reallocation reducing reliance on reserve fleets. • Use agent-based simulation to assess congestion and passenger behavior in disruption. • Benchmark RaaS against bus, taxi, and van bridging in the Paris test case. • Perform sensitivity analysis to confirm the framework’s robustness.