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

RAPID/协作研究：用于 COVID-19 期间人类流动性预测的高频数据收集

• 批准号: 2027744
• 负责人: Qi Wang
• 金额: $ 2.24万
• 依托单位: Northeastern 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: 2020-05-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027744&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Frequency; Data

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.

Covid-19曾经并正在继续大大改变全国各地的企业，学校和许多公共场所的生活。公职人员的建议以及个人担忧和恐惧从根本上改变了日常工作的模式，因为美国人采用了社会疏远，庇护到位，甚至是自我矛盾的做法。这项快速响应研究（快速）项目将提高我们评估和预测由大规模公共卫生危机（例如Covid-19）引起的突然中断下的流动性模式变化的能力。具体的重点将是了解流动性模式的变化以及在与这一重大公共卫生危机相关的展开事件期间塑造这些变化的复杂而动态的决策过程。该项目将通过大大改善Covid-19和未来类似的公共卫生危机面临的公共机构的准备和反应，从而提高国家健康，繁荣和福利。它还将有助于理解和预测人类流动性模式的减少，变化和恢复，以促进人类流动性和城市弹性方面的科学进步，与NSF的使命保持一致。该快速项目的目标是：（1）捕获并最终预测人类流动方式在使用COVID9 PANDECICS PANDECIC MISOR MENIQUIQUES MENIQUIQUES SOMICIQUES IGNIQUE SOMIQUIQUE SONIQUIQUES IGNIQUE SOMIQUIQUE SONIQUIQUES IGNIQUE SIMIQUIQUE SONIQUIQUE SONIQUIQUE SONIQUIQUE SIMIQIOQ中响应的人类流动方式捕获时空变化； （2）通过智能手机应用程序进行高频个人级别的调查，以了解动机，决策和感性的因素，以塑造移动性模式的变化； （3）探索高保真度和高精度人类移动性预测的转换和收敛函数。这项研究的智力优点包括：从与社会疏远，庇护和自我弥补的实践有关的这种公共卫生危机中出现的独特流动性模式；关于动机，决策和情感因素塑造流动性决策的纵向证据的前所未有的聚会；以及使用少量代表性样本进行高保真迁移率预测的创新算法的发展。从项目中获得的数据和知识将增强对城市流动性，旅行需求和资源分配建模的未来研究，并帮助政策制定者评估面临毁灭性灾难（例如Covid-19）的主要城市大都市地区的反应和恢复。项目成果将通过波士顿地区研究计划（BARI）进行分散，东北大学和哈佛大学之间的大学间合作伙伴关系，以及通过Metrolab网络。这项奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来评估的支持。