Sequential Logit Dynamic Travel Demand Model for Hurricane Evacuation

Abstract

Little attention has been given to estimating dynamic travel demand in transportation planning in the past. However, when factors that influence travel are changing significantly over time, such as with an approaching hurricane, dynamic demand and the resulting variation in traffic flow on the network become important. The decision to evacuate in the face of an oncoming hurricane is considered as a series of binary choices over time. A sequential binary logit model is developed to model the probability that the members of a household will evacuate at each time period before hurricane landfall as a function of the household's socioeconomic characteristics, the characteristics of the hurricane, and policy decisions made by authorities as the storm approaches. Data collected in southwest Louisiana after Hurricane Andrew were used to estimate a model that produced dynamic travel demand estimates of hurricane evacuation. On the basis of the results, a sequential logit model appears to be capable of modeling dynamic evacuation demand satisfactorily.