Transportation networks are the backbone of modern society, facilitating economic activity, daily commuting, and access to emergency services. Disruptions, particularly those caused by extreme weather events such as floods, can have severe consequences, including delayed emergency response and limited access to essential services.

This study presents a scalable and generalizable method for analyzing traffic disruptions and optimizing traffic flows. Using this approach, we can identify critical network vulnerabilities and enable strategic adjustments, such as adding or removing capacity, to improve efficiency and resilience under normal and disrupted conditions.

To further enhance our analysis, we are integrating a regional flood model to simulate traffic conditions before and after potential flood events in Germany. This enables us to capture the impact of rerouting and congestion on access to care with a high degree of accuracy. Through computationally efficient simulations at a regional scale, we assess the vulnerability of the healthcare sector to traffic disruptions caused by flooding. Specifically, we quantify changes in the service population of healthcare facilities across Germany and use this as a proxy for assessing flood resilience.