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: 1917019
• 负责人: Samiul Hasan
• 金额: $ 21万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917019&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.

该项目开发了预测自然灾害期间实时人口行为的新方法，有可能以具有成本效益的方式改变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，该项目利用了多个来源的数据，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多个来源的数据可以提高可用于紧急响应预测和改进疏散交通管理的数据的质量。研究成果将与从业者社区分享，以促进应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国家福祉的使命。在这种情况下，好处将是改善应急响应的洞察力，这将挽救生命、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异构数据源结合在一个统一的数据源中。框架 - 为飓风期间动态人口行为建模提供更好的信息。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。它通过开发新颖的信息融合技术来代表人口及其行为，同时利用政府调查和社交媒体数据、文本挖掘方法从社交媒体数据中提取疏散意图，以及疏散交通预测模型来优化交通，从而推进灾害管理的数据科学。资源。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的参与，包括来自代表性不足群体的学生，并计划向当地县市的交通工程师和应急管理官员更广泛地传播成果。该奖项反映了 NSF 的法定使命，并被认为值得支持通过使用基金会的智力优点和更广泛的影响审查标准进行评估。

项目成果

Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma

评估疏散的崩溃风险：对飓风艾尔玛期间收集的数据应用匹配的病例对照方法

• DOI: 10.1016/j.aap.2021.106260
• 发表时间: 2021
• 期刊: Accident Analysis & Prevention
• 影响因子: 5.9
• 作者: Rahman, Rezaur;Bhowmik, Tanmoy;Eluru, Naveen;Hasan, Samiul
• 通讯作者: Hasan, Samiul

Local emergency management's use of social media during disasters: a case study of Hurricane Irma

当地应急管理部门在灾害期间对社交媒体的使用：飓风艾尔玛的案例研究

• DOI: 10.1111/disa.12544
• 发表时间: 2022
• 期刊: Disasters
• 影响因子: 3.2
• 作者: Knox, Claire Connolly

Exploring network properties of social media interactions and activities during Hurricane Sandy

• DOI: 10.1016/j.trip.2020.100143
• 发表时间: 2020-07
• 期刊: Transportation Research Interdisciplinary Perspectives
• 作者: Sandy;A. M. Sadri;Samiul Hasan;S. Ukkusuri;Manuel Cebrian

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using Graph Convolutional Neural Network

• DOI: 10.1007/s42421-023-00073-y
• 发表时间: 2022-02
• 期刊: Data Science for Transportation
• 作者: Rezaur Rahman;Samiul Hasan

Towards reducing the number of crashes during hurricane evacuation: Assessing the potential safety impact of adaptive cruise control systems

• DOI: 10.1016/j.trc.2021.103188
• 发表时间: 2021-07
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Rezaur Rahman;Samiul Hasan;M. Zaki