CAREER: Data-driven Models of Human Mobility and Resilience for Decision Making

职业：数据驱动的人类流动性和决策弹性模型

基本信息

批准号：
1750102
负责人：
Vanessa Frias-Martinez
金额：
$ 50万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750102&HistoricalAwards=false
关键词：
CAREER Data driven Models Human

项目摘要

This project envisions mobile cyber-physical systems (CPS) where people carrying cell phones generate large amounts of location information that is used to sense, compute and monitor human interactions with the physical environment during environmental dislocations. The main objective will be to identify the types of reactions populations have to a given type of shock, providing decision makers with accurate and informative data-driven representations they can use to create preparedness and response plans. Additionally, the outcomes of this project will allow for the development of tools to assess and improve the effectiveness of different types of preparedness and response policies through feedback loops in the mobile CPS. These feedback loops could show how community behaviors during shocks change when policies are re-defined based on the computations of the CPS, and vice-versa. Previous work by the PI and others has already showed that CPS integrating people and cell phones as sensing platforms can be used to collect location information at large scale and to compute, using data mining and machine learning techniques, human mobility behaviors during shocks. However, most of the results are very limited and ad-hoc, lacking any type of serious applicability from a preparedness and response policy. This project will advance the state of the art by developing accurate methods and effective tools for decision-making during shocks in mobile CPS. From a broader impacts perspective, the proposed research will contribute in two areas: (a) real-world deployments, to promote data-driven policy development, data-driven analyses of human behavior, and the use of feedback loops in mobile CPS for decision-making assessment; and (b) the creation of an educational plan and training opportunities in the areas of data science for social good and mobile CPS for decision making.The main outcomes of the project will include novel data-driven methods for mobile CPS that will reliably characterize and predict human mobility patterns and resilience during shocks so as to improve preparedness and response policies. The project will make use of cell phone metadata and social media to achieve the following three objectives: (1) to characterize the types of reactions that communities have to different kinds of shocks using real-time data from mobile CPS, which would allow for the development of more adequate preparedness policies to be ready for future events; (2) to create predictive methods to forecast the impact that shock management policies would have on human mobility behaviors and community resilience during a shock, using human behavioral information from the CPS feedback loop when different policies are applied (either in real-time or in batch processing); and (3) to evaluate the transferability of the types of reactions and predictive methods across different shocks, spatio-temporal scales and data sources in mobile CPS, which would provide decision makers with the possibility of analyzing behaviors and resilience in communities where cell phone metadata in the CPS is not fully available. From an intellectual merit perspective, the proposed methods will advance the state of the art in data analytics and real-time systems for CPS in the area of Smart and Connected Communities.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.

该项目设想了移动网络物理系统（CPS），携带手机的人们可以生成大量位置信息，用于感知、计算和监控环境混乱期间人类与物理环境的交互。主要目标是确定人群对特定类型冲击的反应类型，为决策者提供准确且信息丰富的数据驱动表示，他们可以使用它们来制定准备和应对计划。此外，该项目的成果将允许开发工具，通过移动 CPS 中的反馈循环来评估和提高不同类型的准备和响应政策的有效性。这些反馈循环可以显示当基于 CPS 的计算重新定义政策时，冲击期间的社区行为如何变化，反之亦然。 PI 和其他人之前的工作已经表明，将人和手机集成为传感平台的 CPS 可用于大规模收集位置信息，并使用数据挖掘和机器学习技术来计算冲击期间的人类移动行为。然而，大多数结果都非常有限和临时性，缺乏准备和应对政策的任何类型的严肃适用性。该项目将通过开发准确的方法和有效的工具来在移动 CPS 冲击期间进行决策，从而推进最先进的技术。从更广泛的影响角度来看，拟议的研究将在两个领域做出贡献：（a）现实世界的部署，以促进数据驱动的政策制定、数据驱动的人类行为分析以及使用移动 CPS 中的反馈循环进行决策- 进行评估； (b) 在数据科学促进社会公益和用于决策的移动 CPS 领域制定教育计划和培训机会。该项目的主要成果将包括用于移动 CPS 的新型数据驱动方法，该方法将可靠地表征和预测冲击期间的人员流动模式和复原力，以改进备灾和应对政策。该项目将利用手机元数据和社交媒体来实现以下三个目标：(1) 使用移动 CPS 的实时数据来描述社区对不同类型冲击的反应类型，这将允许制定更充分的准备政策，为未来的事件做好准备； (2) 创建预测方法来预测冲击期间冲击管理政策对人类流动行为和社区复原力的影响，使用应用不同政策时来自 CPS 反馈循环的人类行为信息（实时或分批）批处理）； (3) 评估移动 CPS 中不同冲击、时空尺度和数据源的反应类型和预测方法的可迁移性，这将为决策者提供分析手机元数据所在社区的行为和复原力的可能性CPS 中的功能并不完全可用。从智力价值的角度来看，所提出的方法将推动智能和互联社区领域 CPS 数据分析和实时系统的最先进水平。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Constructing Evacuation Evolution Patterns and Decisions Using Mobile Device Location Data: A Case Study of Hurricane Irma

使用移动设备位置数据构建疏散演变模式和决策：飓风艾尔玛的案例研究

DOI：
发表时间：
2021-02-24
期刊：
ArXiv
影响因子：
0
作者：
Aref Darzi;V. Frías;Sepehr Ghader;H. Younes;Lei Zhang
通讯作者：
Lei Zhang

Modeling and predicting evacuation flows during hurricane Irma.

模拟和预测飓风艾尔玛期间的疏散流量。

DOI：
10.1140/epjds/s13688-020-00247-6
发表时间：
2020-09
期刊：
EPJ data science
影响因子：
3.6
作者：
Hong, Lingzi;Frias
通讯作者：
Frias

Characterization of internal migrant behavior in the immediate post-migration period using cell phone traces

使用手机痕迹描述移民后不久的内部移民行为特征

DOI：
10.1145/3287098.3287119
发表时间：
2019-01-04
期刊：
Proceedings of the Tenth International Conference on Information and Communication Technologies and Development
影响因子：
0
作者：
Lingzi Hong;Jiahui Wu;E. Frías;A. Villarreal;V. Frías
通讯作者：
V. Frías

Enhancing Short-Term Crime Prediction with Human Mobility Flows and Deep Learning Architectures". EPJ Data Science.

利用人员流动和深度学习架构增强短期犯罪预测”。EPJ 数据科学。