Hurricane evacuation is a complex dynamic process and a better understanding of the factors which influence the evacuation behavior of the coastal residents could be helpful in planning a better evacuation policy. Traditionally, the various aspects of the household evacuation decisions have been determined by post-evacuation questionnaire surveys, however, these surveys have seen a deterioration in the quality of the data due to a gradual decrease in response rates in recent years, which may lead to non-response bias. Increased activity of users on social media, especially during emergencies, along with the geo-tagging of the posts, provides an opportunity to gain insights into user's decision-making process, as well as to gauge public opinion and activities using the social media data as a supplement to the traditional survey data. This paper leverages the geo-tagged Tweets posted in the New York City (NYC) and Jacksonville, FL in wake of Hurricane Sandy and Matthew respectively to understand the evacuation behavior of the Twitter users and compare them with that of the survey respondents. We design the Twitter user classification problem as a novel HMM modeling framework to classify them into one of the three categories: outside evacuation zone, evacuees, and non-evacuees. We compare the demographic composition (age, gender, and race/ethnicity) and spatial coverage of Twitter users with that of the survey respondents to highlight the complementary nature of the two data sources, which when combined give a representative sample of the population. We analyze the GPS coordinates of the tweets by evacuees to understand evacuation and return time and evacuation location patterns and compared them with survey respondents. The techniques presented in this paper provide an alternative (fast and voluntary) source of information for modeling evacuation behavior during emergencies, which is complementary in terms of demographics and spatial distribution as compared to the traditional surveys and could be useful for authorities to plan a better evacuation campaign to minimize the risk to the life of the residents of the emergency hit areas.

飓风疏散是一个复杂的动态过程，更好地了解影响沿海居民疏散行为的因素可能有助于制定更好的疏散政策。传统上，家庭疏散决定的各个方面都是通过撤离后问卷调查来确定的，但是，由于近年来响应率的逐渐下降，这些调查发现数据质量有所下降，这可能导致无回应偏差。用户在社交媒体上的活动增加，尤其是在紧急情况下，以及帖子的地理标记，提供了一个机会，可以深入了解用户的决策过程，并使用社交媒体数据来评估公众舆论和活动。传统调查数据的补充。纽约市（NYC）和佛罗里达州杰克逊维尔，分别是在飓风桑迪和马修之后，他们了解了Twitter用户的疏散行为，并将其与调查对象的疏散行为进行了比较。我们将Twitter用户分类问题设计为一种新颖的HMM建模框架，以将其分为以下三类之一：外部疏散区，疏散人员和非疏散人员。我们将Twitter用户的人口组成（年龄，性别和种族/族裔）和空间覆盖率与受访者进行比较，以突出显示这两个数据源的互补性，将两者结合起来可得出具有代表性的人口样本。我们分析了疏散人员的推文的GPS坐标，以了解疏散和返回时间以及疏散位置的方式，并将其与调查对象进行了比较。