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Demand generation and network loading models under hurricane evacuation conditions, which include more guided actions compared to unpredictable disasters, are crucial yet challenging components of evacuation studies. This study has two major goals: (1) Conduct a comprehensive and critical review of demand generation and network loading models under hurricane conditions to understand advantages and disadvantages of available approaches, (2) Assess network-wide impacts of these different demand generation and loading models using an analytical system optimal dynamic traffic assignment model. The review of demand generation and loading models under hurricane conditions revealed the existence of three distinct yet frequently used approaches namely, S-curves, Tweedie’s and sequential logit models (SLM). System Optimal Dynamic Traffic Assignment (SO-DTA) formulation originally proposed by Ziliaskopoulos (25) is then used to model a simplified version of the Cape May network. This multiple origin-single destination SO-DTA case study model is then solved for different demand patterns generated using the three methods described in the first portion of the paper. S-curve and Tweedie’s approaches are found to cause unrealistically large travel time delays mainly due to the fact that they generate demand patterns that are loaded within few hours only. This is clearly not a realistic assumption and is not observed in real data either. On the other hand, demand patterns generated using SLM that are spread over much longer periods of time are found to cause more realistic network delays when they are assigned onto the case study network using the developed SO-DTA model.
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