# Modeling Evacuees' Intended Responses to a Phased Hurricane Evacuation Order
## 1 Introduction  
Severe traffic jams occurred in Florida during Hurricane Irma (2017) because over six million people were ordered to evacuate using a road system that could not manage the associated traffic [1]. Westbound travel times from Jacksonville Beach to Tallahassee were doubled during the evacuation, and northbound travel from Florida to Georgia was even worse [2]. Theoretically, phased (staged or sequenced) evacuation orders could have been issued to increase evacuation efficiency and reduce travel times by spreading the demand over time [3]. This demand-management strategy identifies and sequences different \"evacuation phases\" (generally) by risk level and assigns different suggested evacuation times to each. Typically this is done either geographically, with higher-risk zones starting first [4, 5] or by evacuating people with distinct characteristics first, such as tourists and special-needs residents prior to other residents [6, 7]. Phased evacuation strategies exist in the emergency management plans of several coastal states (e.g., Florida,Louisiana, and Texas) that are affected by hurricanes relatively frequently [4; 8; 9]. Even so, the effectiveness of this strategy is not well understood given that household decision-making has a significant effect on the strategy's effectiveness. Furthermore, few studies have examined how households react to this approach directly. Prior studies of human decision-making for evacuations have well documented that household behavioral responses to evacuation orders vary. From the household point of view, responding to such orders is complicated, and decisions are ultimately determined by a multi-dimensional set of factors that influence their decisions, including messaging, past experiences, risk perception, resources, personalization, and many other factors. These factors interact with each other to drive whether to evacuate and when to evacuate, among other choices [10; 11]. Authorities often view these households' responses in simple terms of compliance or non-compliance to the order. In looking at such behavior, it is clear that not everyone from high-risk areas complies with evacuation notices [12] and that from event to event there are variations in the levels of compliance. One recent study suggested that this ranges from 12% to 97% in different cases of hurricane evacuations [13]. Furthermore, not everyone in relatively low-risk areas without evacuation notices stays [12]. Among those who evacuate, some do so before the evacuation orders officially begin, while others depart later--after the hurricane path becomes more certain [14]. Considering these behavioral patterns, it is challenging to predict what proportion of the population is likely to comply with phased evacuation. This study begins to address this gap by examining the rate of intended phased evacuation compliance and identifying factors associated with this compliance based on responses from a household evacuation behavioral intent survey of 415 households from the Hampton Roads, VA area. While an imperfect approximation of behavior during a real hurricane, such behavioral intention data provide an improved insight into this issue over the common convention of assuming 100% compliance with orders or arbitrarily setting compliance factors with little empirical evidence. This study presents statistical models to understand household compliance behavior. The compliance rates and the estimated latent class logit model can help researchers and evacuation planners develop more realistic transportation models to analyze the impact of phased evacuations. The model's parameters and related interpretations also lead to suggestions for emergency management agencies about what they can do to motivate greater compliance in practice. The remainder of this paper is organized as follows. Section 2 describes how phased evacuations are implemented in current practices and provides insights from related research.