Collaborative Research: Predicting Real-time Population Behavior during Hurricanes Synthesizing Data from Transportation Systems and Social Media

合作研究：综合交通系统和社交媒体数据预测飓风期间的实时人口行为

• 批准号: 2133960
• 负责人: Aron Culotta
• 金额: $ 9万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133960&HistoricalAwards=false
• 关键词: Collaborative; Research; Predicting; Real; time

This project develops new methods to forecast real-time population behavior during natural disasters, potentially transforming the current state of emergency response in a cost-effective way. To understand how individuals, infrastructure systems, and emergency services should prepare and respond during such disasters, this project utilizes data available from multiple sources including from transportation systems and online social media. Using innovative data science approaches to integrate data from multiple sources increases the quality of the data available for emergency response prediction and improved evacuation traffic management. Research outputs will be shared with the practitioner community to facilitate improved decision making for emergency agencies in hurricane evacuation and disaster management. This scientific research contribution thus supports NSF's mission to promote the progress of science and to advance our national welfare. In this case, the benefits will be insights to improve emergency response, which will save lives, economic losses, and reduce panic, anger and confusion during a future event.The project combines heterogeneous data sources from transportation systems and social media, in a unified framework-providing better information for modeling dynamic population behavior during hurricanes. To accurately predict evacuation demand, this project leverages large-scale real-time data, rarely used by existing emergency decision support tools. It advances the data science of disaster management by developing novel information fusion techniques to represent population and its behavior while employing government survey and social media data, text-mining approaches to extract evacuation intent from social media data, and evacuation traffic prediction models to optimize transportation resources. Through its innovative data gathering and modeling approaches, this project will enhance our ability to deal with future hurricanes. The project engages a broader participation of graduate and undergraduate students including from under-represented groups and plans a broader dissemination of results to traffic engineers and emergency management officials from local counties and cities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目开发了新的方法来预测自然灾害期间的实时人口行为，潜在地以具有成本效益的方式改变了当前的应急响应状态。为了了解个人、基础设施系统和应急服务应如何在此类灾难中做好准备和应对，该项目利用了来自多种来源的数据，包括交通系统和在线社交媒体。使用创新的数据科学方法整合来自多个来源的数据，可提高可用于应急响应预测和改进疏散交通管理的数据质量。研究成果将与实践者社区分享，以促进紧急机构在飓风疏散和灾害管理方面改进决策。因此，这项科学研究贡献支持了NSF促进科学进步和增进我们国家福利的使命。在这种情况下，好处将是改进应急响应的见解，这将拯救生命、经济损失，并减少未来事件中的恐慌、愤怒和困惑。该项目将来自交通系统和社交媒体的不同数据源结合在一个统一的框架中-为建模飓风期间的动态人口行为提供更好的信息。为了准确预测疏散需求，该项目利用了现有应急决策支持工具很少使用的大规模实时数据。它通过开发新的信息融合技术来表示人口及其行为，同时利用政府调查和社交媒体数据、文本挖掘方法来从社交媒体数据中提取疏散意图，并通过疏散交通预测模型来优化交通资源，从而推动了灾害管理的数据科学。通过其创新的数据收集和建模方法，该项目将增强我们应对未来飓风的能力。该项目吸引了更广泛的研究生和本科生参与，包括来自代表性不足的群体，并计划将结果更广泛地传播给交通工程师和来自当地县市的应急管理官员。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Forecasting COVID-19 Vaccination Rates using Social Media Data

使用社交媒体数据预测 COVID-19 疫苗接种率

• DOI: 10.1145/3543873.3587639
• 发表时间: 2023
• 期刊: WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
• 作者: Li, Xintian;Culotta, Aron
• 通讯作者: Culotta, Aron

Identifying Hurricane Evacuation Intent on Twitter

• DOI: 10.1609/icwsm.v16i1.19320
• 发表时间: 2022-05
• 作者: Xintian Li;Samiul Hasan;A. Culotta