Cities like Rotterdam, facing increasing flood risks, require integrated planning tools that account for the interplay between flooding and urban mobility. Despite advances in hydrodynamic and agent-based transport modelling, practical frameworks for coupling these models remain underdeveloped. This thesis develops an XLRM (eXogenous uncertainties, Levers, Relationships, Metrics) framework to guide the integration of flood simulation models (e.g., 3Di) with transport modelling (e.g., MATSim/SUMO) for robust decision making.
Drawing on literature review and expert interviews, the research identifies key sources of uncertainty and policy levers relevant to Rotterdam’s flood–transport system. The main contribution is the formalisation of the Relationships (R) layer: a set of rule-based protocols translating flood-model outputs into dynamic adjustments in transport models, based on empirically calibrated depth–speed functions and iterative data exchange at operational time steps. Optional feedback loops allow planners to test intervention timing and road management strategies under uncertainty.
Validation with domain specialists confirmed the framework’s technical soundness, logical clarity, and practical transferability. The resulting XLRM register offers planners a blueprint for embedding flood–transport interactions within digital-twin environments, enabling stress testing of adaptation strategies and supporting resilient, evidence-based infrastructure planning under climate uncertainty.