This thesis presents a novel framework for assessing failure propagation and its economic consequences in road transport networks, focusing on both structural vulnerability and real-time performance degradation. Unlike traditional models that treat disruptions as static or isolated events, this research introduces dynamic failure profiles (Linear, Exponential, and Compound), and assesses their impact using a hybrid simulation approach. By integrating macroscopic and microscopic transport models with graph theory, and embedding failure behaviour through a custom Aimsun Advanced Programming Interface (API), the methodology enables fine-grained simulation of cascading disruptions.

Applied to Lisbon’s coastal transport network under flooding scenarios, the study demonstrates that linear failure profiles caused up to 41.9% increase in total travel time, compared to 19.7% for sudden failures, revealing that progressive degradation can produce more severe impacts than abrupt disconnections. Simultaneous multi-link failures showed non-linear cost escalations, emphasizing the importance of accounting for compound disruption dynamics in resilient infrastructure planning.

Economic consequences were quantified using three cost models: Value of Time (VOT), Vehicle Operating Cost (VOC), and the Portuguese Road User Cost (PTRUC). Linear failures generated the highest Road User Cost (RUC) across all methods, with PTRUC outputs indicating a cost increase of 278% compared to the baseline. When Traffic Management (TM) costs were included, the total cost rose by an additional 12.4%, underlining the importance of integrating operational response expenditures in consequence models. The study also forecasts that electrification trends could reduce operating costs by 15-23% in similar scenarios by 2050.

While conclusions related to network topology and urban flooding risks are context-specific, the simulation framework, failure typologies, and cost classification system provide a transferable model for assessing disruptions in other dense, hazard-prone transport systems. This thesis contributes both theoretical and practical innovations to the discipline of transport resilience analysis. It highlights the complexity of network behaviour during failures and emphasizes the necessity of using multiple performance indicators to grasp the impact of disruptions fully.