This project will develop new methodologies for understanding hurricane evacuations by integrating behavioral modeling of household decision making with dynamic transportation modeling. By understanding evacuation decision making, the research will contribute to improving the efficiency of hurricane evacuation.
This project will develop novel modeling approaches to: (1) Estimate the social, demographic and cultural characteristics that influence household evacuation decision making from surveys of Miami-Dade County residents; (2) Understand how people synthesize evacuation warnings up to the time they make the decision to evacuate; (3) Assess the influence of household decisions based on spatial location; (4) Estimate the temporal variation of evacuees from the time of warning; (5) Determine the optimal time of departure, optimal route and destination choice based on the temporal demand patterns; (6) Incorporate behavioral rules obtained from social science analysis to simulate the transportation system impacts; and (7) Identify ways to distribute the obtained results from multiple hurricane scenarios to stakeholders.
The project will use multidisciplinary approaches to understand hurricane evacuation by bringing together approaches from social science, network optimization, agent-based modeling, transportation operations, stochastic optimization and hurricane emergency response.
The goal of this project is to develop an integrated model for hurricane evacuation combining household level decision making with an agent based traffic simulation model. Such an integration will allow the characterization rich household behavior and measure key performance metrics related to evacuation clearance times. Furthermore, it will allow the development and testing of meaningful policies. The project is divided into two main phases: (1) Household Level Decision Making and (2) Agent Based Traffic Simulation Modeling. New Data is collected from Miami Beach residents to understand the household decision-making and this is used to estimate various behavioral models. Then the behavioral models are integrated with a novel agent based traffic simulation model to measure average travel clearance time, the effects on the different populations in the city and the computational efficiency of the tool. On the behavioral side, a household level model was developed. The household level model captured various decision making attributes and identified the factors that govern the decision making related to: (1) which households evacuate; (2) timing of the evacuation; (3) destination type choice; (4) the routing election; (5) shadow evacuation (6) number of vehicles that households use; (7) pre-evacuation and enroute evacuation activity participation. This is one of the first projects which capture these different behavioral dimensions at the household level for understanding hurricane evacuation. Survey data from Miami beach residents was collected to estimate these models. The behavioral models are integrated into a household level agent based traffic simulation. A novel agent based model was developed which can capture the car-following and lane changing behavior of traffic. Data modeling was performed to obtain the correct level of network detail and ensure network integrity. Different test scenarios for the base case and projected future years was conducted to measure various performance metrics. This agent based model is one of the very few tools that are available to measure the effectiveness of household level decision-making in hurricanes and in arriving at novel policies to maximize evacuation efficiency in hurricanes.
