Comparison of Time-Dependent Sequential Logit and Nested Logit for Modeling Hurricane Evacuation Demand
Ravindra Gudishala, Chester G. Wilmot
Abstract
Ravindra Gudishala, Chester G. Wilmot
Abstract
Models that predict hurricane evacuation demand can play a crucial role in developing and evaluating alternative evacuation policies and plans. However, to evaluate alternative policies effectively, evacuation demand models should be sensitive to time varying characteristics of a storm and the contextual conditions surrounding an evacuee. The time-dependent sequential logit is one such model, but it makes use of restrictive assumptions about the dynamic choices made by evacuees. A new model, a time-dependent nested logit model, relaxes those assumptions. It was formulated and derived in this study, and its performance was then compared with that of the time-dependent sequential logit model by applying both models to data from Hurricane Gustav. The results indicated that the time-dependent nested logit model has better predictive capability than the time-dependent sequential logit model.
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Models that predict hurricane evacuation demand can play a crucial role in developing and evaluating alternative evacuation policies and plans. However, to evaluate alternative policies effectively, evacuation demand models should be sensitive to time varying characteristics of a storm and the contextual conditions surrounding an evacuee. The time-dependent sequential logit is one such model, but it makes use of restrictive assumptions about the dynamic choices made by evacuees. A new model, a time-dependent nested logit model, relaxes those assumptions. It was formulated and derived in this study, and its performance was then compared with that of the time-dependent sequential logit model by applying both models to data from Hurricane Gustav. The results indicated that the time-dependent nested logit model has better predictive capability than the time-dependent sequential logit model.
Key concepts: Logit, Nested logit, Logistic regression, Mixed logit, Econometrics, Computer science, Demand forecasting, Operations research