Multiscale Model for Hurricane Evacuation and Fuel Shortage

Hurricanes are powerful agents of destruction with significant socioeconomic impacts. High-volume mass evacuations, disruptions to the supply chain, and fuel hoarding from non-evacuees have led to localized fuel shortages lasting several days during recent hurricanes. Hurricane Irma in 2017, resulted in the largest evacuation in the nation affecting nearly 6.5 million people and saw widespread fuel shortages throughout the state of Florida. While news reports mention fuel shortages in several past hurricanes, the crowd source platform Gasbuddy has quantified the fuel shortages in the recent hurricanes. The analysis of this fuel shortage data suggested fuel shortages exhibited characteristics of an epidemic. Fundamentally, as fueling stations were depleted, the latent demand spread to neighboring stations and propagated throughout the community, similar to an epidemiological outbreak. In this paper, a Susceptible- Infected –Recovered (SIR) epidemic model was developed to study the evolution of fuel shortage during a hurricane evacuation. Within this framework, an optimal control theory was applied to identify an effective intervention strategy. Further, the study found a linear correlation between traffic demand during the evacuation of Hurricane Irma and the resulting fuel shortage data from Gasbuddy. This correlation was used in conjunction with the State-wide Regional Evacuation Study Program (SRESP) surveys to estimate the evacuation traffic and fuel shortages for potential hurricanes affecting south Florida. The epidemiological SIR dynamics and optimal control methodology was applied to analyze the fuel shortage predictions and to develop an effective refueling strategy

Fuel Shortages During Hurricanes: Epidemiological Modeling and Optimal Control

Hurricanes are powerful agents of destruction with significant socioeconomic impacts. A persistent problem due to the large-scale evacuations during hurricanes in the southeastern United States is the fuel shortages during the evacuation. Fuel shortages often lead to stranded vehicles and exacerbate the evacuation efforts. Computational models can aid in emergency preparedness and help mitigate the impacts of hurricanes. In this thesis, the hurricane fuel shortages are modeled using the Susceptible-Infected-Recovered (SIR) epidemic model. Crowd-sourced data corresponding to Hurricane Irma and Florence are utilized to parametrize the model. An estimation technique based on Unscented Kalman filter (UKF) is employed to evaluate the SIR dynamic parameters. Finally, an optimal control approach for refueling based on a vaccination analogue is presented to effectively reduce the fuel shortages under a resource constraint. The control model estimates the duration and the level of intervention required to mitigate this epidemic. This approach could be useful for emergency management of future hurricanes. A predictive model is then proposed where the UKF can be utilized to evaluate the SIR dynamic parameters from incoming fuel shortage during the initial stages of the hurricane. Due to the nature of the Ordinary Differential Equations (ODE) of SIR dynamics, only one of the parameters can be accurately estimated from the data collection of initial stages of the evacuation. The Basic Reproduction number (R0) value is then varied to produces predictive trends and the optimal refueling strategy is applied to these probable fuel shortage trends to demonstrate possible countermeasures