| description abstract | The hurricane-forecast-evacuation system is complex and dynamic, making it difficult to diagnose potential challenges and implement effective intervention strategies to ensure successful evacuations for everyone. Here we use an agent-based modeling framework to explore how changing different components of the system affects key evacuation outcomes. Called the forecasting laboratory for exploring the evacuation-system (FLEE), this modeling framework integrates high-level representations of the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between elements (forecasts and warning information, traffic). Using FLEE, we investigate the simulated effects of changing the number of cars on the road network (changing evacuation demand), implementing approximations to different evacuation management strategies and policies (contraflow, evacuation order timing), and changing population characteristics (population growth and distribution), all for two real scenarios (Irma, Dorian) and one simulated storm (rapid-onset version of Irma). After comparing and validating FLEE’s evacuation outcomes with real-world empirical data, we use FLEE to explore how simulated changes impact evacuation success overall, how the changes compare, and how impacts from the changes vary across forecast scenarios and regions. Through the experiments, we demonstrate the power of these types of frameworks as tools for exploring the forecast-evacuation system across many scenarios, and identify potential next steps to better support researchers, practitioners, and policymakers. Because hurricane evacuations involve many factors and uncertainties, it is often difficult to diagnose potential challenges and implement intervention strategies to ensure everyone can evacuate safely. This research provides a new attempt to help address these needs, by integrating the many physical-social factors into a single modeling framework where we can explore potential challenges and interventions from a new perspective. Using the model, we look at how evacuation outcomes vary with the number of cars on the road network, evacuation management strategies and policies, changes in population characteristics, and different forecast scenarios. We look at which changes are most important to evacuation success, if that changes across forecast scenarios, and if some areas in the model are more impacted than others. Throughout the analyses, we demonstrate how the modeling framework is a powerful research tool capable of studying evacuations across many scenarios. We also discuss next steps for improving the models to support researchers and practitioners working to improve evacuations and save lives. | |
