RAPID/Collaborative Research: High-Frequency Data Collection for Human Mobility Prediction during COVID-19

COVID-19已经并将继续极大地改变数百万美国人的生活，因为全国各地的企业，学校和许多公共场所都关闭了。公职人员的建议沿着个人的担忧和恐惧，从根本上改变了日常生活的模式，美国人采取了社交距离、就地避难甚至自我隔离的做法。这个快速反应研究（RAPID）项目将提高我们评估和预测在大规模公共卫生危机（如COVID-19）造成的突然中断下流动模式变化的能力。具体重点将是了解流动模式的变化以及在与这一重大公共卫生危机相关的事件发生期间塑造这些变化的复杂和动态决策过程。该项目将通过大大改善公共机构面对COVID-19和未来类似公共卫生危机的准备和应对，促进国家健康、繁荣和福利。该项目的目标是：（1）利用社交媒体数据挖掘技术，捕捉并最终预测COVID-19大流行后人类流动模式的时空变化;（2）利用社交媒体数据挖掘技术，通过对人类流动模式的分析，预测人类流动模式的时空变化;（3）利用社交媒体数据挖掘技术，预测人类流动模式的时空变化。（2）通过智能手机应用程序进行高频率的个人层面调查，以了解影响移动模式变化的动机、决策和情感因素;（3）探索转换和收敛函数，以实现高保真和高准确度的人类移动预测。这项研究的智力价值包括：发现了与社交距离，庇护和自我隔离做法有关的公共卫生危机中出现的独特流动模式;前所未有地收集了有关影响流动决策的动机，决策和情感因素的纵向证据;以及开发了创新算法，使用小型代表性样本进行高保真流动预测。从该项目中获得的数据和知识将加强未来对城市流动性、出行需求和资源分配建模的研究，并帮助政策制定者评估面临COVID-19等毁灭性灾难的主要城市大都市区的应对和恢复。项目成果将通过波士顿地区研究倡议（巴里），东北大学和哈佛大学之间的大学间合作伙伴关系，并通过MetroLab网络进行传播。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估。

COVID-19 has and is continuing to dramatically alter the lives of millions of Americans as businesses, schools, and many public places have closed around the country. Recommendations of public officials along with individual concerns and fears have fundamentally changed the pattern of daily routines as Americans have adopted the practices of social distancing, sheltering in place, and even self-quarantine. This Rapid Response Research (RAPID) project will improve our ability to assess and predict changes in mobility patterns under sudden disruptions caused by large-scale public health crises such as COVID-19. The specific focus will be to understand changes in mobility patterns and the complex and dynamic decision-making process shaping these changes during the unfolding events associated with this major public health crisis. The project will advance the national health, prosperity, and welfare by greatly improving the preparedness and responses of public agencies facing COVID-19 and future similar public health crises. It will also help understand and predict reduction, change, and recovery of human mobility patterns promoting the progress of science in human mobility and urban resilience, in alignment with the mission of NSF.The objectives of this RAPID project are to: (1) capture and ultimately predict spatiotemporal changes in the patterns of human mobility in response to the COVID-19 pandemic using social media data mining techniques; (2) perform high-frequency individual-level surveys via a smartphone app to understand motivational, decisional, and sentimental factors shaping changes in mobility patterns; and (3) explore conversion and convergence functions for high fidelity and high accuracy human mobility prediction. The intellectual merits of this research include: the discovery of unique mobility patterns emerging from this public health crises related to social distancing, sheltering, and self-quarantine practices; the unprecedented gathering of longitudinal evidence about the motivational, decisional and sentimental factors shaping mobility decisions; and the development of innovative algorithms of using a small representative sample for high-fidelity mobility prediction. The data and knowledge gained from the project will enhance future studies on urban mobility, travel demand and resource allocation modeling, and help policymakers assess the response and recovery of major urban metropolitan area facing a devastating disaster such as COVID-19. Project outcomes will be disseminated through the Boston Area Research Initiative (BARI), an inter-university partnership between Northeastern University and Harvard University, and through the MetroLab Network.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.