This grant funds the development of an approach that more accurately predicts evacuation time for no-notice events by explicitly integrating household interactions and characteristics with transportation modeling and simulation. Original data on household decision-making will be gathered through in-depth personal interviews, which will reveal household evacuation planning, decision-making processes, the degree to which households optimize their plans, and their transportation needs, reliance on communications technology, and dependence on schools to evacuate children. Additional interviews with schools will identify their evacuation plans and coordination with parents. The data will inform new behavior models of household member interactions and decision-making in a no-notice evacuation. These new models will then be integrated with transportation simulation to examine effects on traffic and evacuation times. Finally, using the traffic information, an original mathematical program and solution methodology will be developed for schools and other entities to select optimal relocation sites for people within their care that facilitate collection by family members. The models will be tested for three hazardous materials incidents and evaluated for home evacuation and workplace evacuation with and without home clearance.
This research integrates social and transportation engineering aspects of evacuation to form a holistic view and lays the groundwork for new models that capture previously overlooked complex travel patterns and more accurately predict evacuation times. This study identifies and addresses the needs of people without cars, generates a greater understanding of how household interactions and gender roles play a role in evacuation decision-making and how households view their emergency mode and route choices, and promotes dialogue between schools and parents for the collection of children during an evacuation. The optimization methodology for relocating children or other dependent groups facilitates their collection and expedites the evacuation. By addressing evacuation from both the household and transportation network levels, this project reveals the microscopic details needed to accurately answer questions at the macroscopic level.
