Hurricane evacuation decision model in a stochastic dynamic network

In the case of a hurricane event, uncertainties and corresponding impacts during a storm event can quickly cascade. Failure to incorporate these uncertainties can significantly affect the efficiency and effectiveness of the emergency responses. Note that, storm hazards, such as strong winds, torrential rain, and storm surges, can inflict significant damage on the road network, affect population’s ability to move during the storm event. A methodology is proposed to generate a sequence of actions that simultaneously solve the evacuation flow scheduling and suggested routes which minimize the total flow time, or the makespan, for the evacuation process from origins to destinations in the resulting stochastic time-dependent network. The methodology is implemented for the 2017 Hurricane Irma case study to recommend an evacuation policy for Manatee county, FL. The results are compared with evacuation plans for assumed scenarios and suggest that evacuation recommendations based on single scenario reduces the effectiveness of the evacuation procedure. Overall contribution of the research presented here is the new methodology to determine the quickest evacuation schedule and routes under the uncertainties within the resulting stochastic transportation networks.