Successful evacuations from metropolitan areas require optimizing the transportation network, monitoring conditions, and adapting to changes. Evacuation plans seek to maximize the city's ability to evacuate traffic to flee the endangered region, but once an evacuation begins, real time events degrade even the best plans.

To better understand behavioral responses made during a hurricane evacuation, a survey of potential evacuees obtained data on demographics, driving characteristics, and the traffic information considered prior to and during an evacuation. Analysis showed significant levels of correlation between demographic factors (e.g., gender, age, social class, etc.) and self-assessed driver characteristics, but limited correlation with the decision to take an alternate route. Survey results suggest evacuees' decisions to divert are functions of the length of time a driver has been in congestion, the amount of travel information provided, and its method of delivery. This association differs significantly from those identified by other studies that focused on routine, non-evacuation, conditions. A decision-making model that forecasts decision tendencies using these factors was created.

The model was integrated in and tested using a dynamic evacuation simulation. The combined model and simulation allow assessment of the impacts traveler information content, timing, and method of delivery have on traffic flow and evacuation times, imitating the impact of traffic information systems. The effectiveness of alternate route use was assessed by measurements of total vehicle volumes processed and queue persistence. Effectiveness was highly dependent on the road network in the immediate vicinity, especially the number of accesses to the alternate route and vehicle capacity on the alternate route and accesses. Integration of the decision-making model in a dynamic hurricane evacuation simulation is unique to this study.

This study yields a greater understanding of evacuee decisions and factors associated with related travel decisions. It provides the novel integration of a behavioral model and a dynamic evacuation simulation, increasing the realism of evacuation planning and providing a valuable tool supporting the decision process. Understanding gained may contribute to reduced evacuation times and enhanced public safety.