Sequential Logit Dynamic Travel Demand Model and Its Transferability

Abstract

A dynamic hurricane evacuation travel demand model was estimated by using sequential logit with the data from Hurricane Floyd in South Carolina. The model was estimated on a random sample of 75% of the observations and applied to the remaining 25% as a test. In the test data, a total of 241 evacuations were predicted when 246 were observed, and the model estimated the number of evacuations in each 2-h period over 4 days with a root-mean-square error of 2.79 evacuations. Evacuation orders were modeled as a time-dependent variable. This significantly enhanced model performance over that achieved with evacuation orders as a stationary variable in previous work and provided the capability to analyze the impact of the type and timing of evacuation orders. That capability permits analyzing staged evacuation, in which areas are directed to evacuate in a sequence that optimizes network use. A model estimated on Hurricane Floyd evacuation data was transferred to Southeast Louisiana; its predictions were similar to evacuation behavior observed during Hurricane Andrew. With updating of the alternative specific constant of the transferred model to ensure the correct prediction of the total number of evacuations, the model predicted evacuation with a root-mean-square error of 4.53 evacuations per 6-h period. It was discovered that applying the same distance function to the two different hurricanes was a major source of error in model transfer. The representation of distance and its interactions with other variables need to be investigated further. The procedures and the information needed for model update warrant further study.