Collaborative Research: A Whole-Community Effort to Understand Biases and Uncertainties in Using Emerging Big Data for Mobility Analysis

协作研究：全社区共同努力，了解使用新兴大数据进行出行分析时的偏差和不确定性

• 批准号: 2114260
• 负责人: Cynthia Chen
• 金额: $ 54.25万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114260&HistoricalAwards=false
• 关键词: Collaborative; Research; Whole; Community; Effort

This NSF grant will quantify the biases and uncertainties associated with human mobility patterns when they are derived from big mobile data such as cell phone data, mobile app data and social media data. Information on human mobility patterns, or where and how Americans live, work and go about their daily activities, is the basis of hundreds of billions' investment for the nation's transportation infrastructures. These investment decisions have a direct impact on Americans’ upward social mobility, health, and well-being. The project is motivated by two factors: first, big mobile data increasingly replaces traditional household survey data in mobility analysis; and second, big mobile data is fundamentally unrepresentative (and biased) and a direct application of the results derived from such data can have substantial negative impacts on Americans’ health, prosperity and welfare. Novel education and outreach activities organically integrated with the research, including a collaboration with the Boston Museum of Science for a digital exhibit on mobility tales around the world, and a mini-track competition with MetroLab on “future mobility and justice for students around the world.”In addition to quantifying the biases and uncertainties associated with mobility patterns, this grant will also identify the extent those biases and uncertainties are affected by a number of factors, e.g., data characteristics, the modeling techniques used, and geographical differences. More specifically, the project comprises three research thrusts. Thrust 1 engages stakeholders and the research community to develop a solicitation calling for mobility labs around the world to submit critical mobility metrics, using their own data and methods. Thrust 2 involves the development of two novel methodologies: a coupled Bootstrap computational framework to quantify biases and uncertainties associated with derived mobility metrics and a rule-based learning framework to handle sparsity issues that likely arise during the analysis stage. Thrust 3 involves all participating labs for results summarization and dissemination. The project will unite multiple disciplines from transportation engineering to systems engineering, computer/information science, and social science in a concerted effort for better understanding the uncertainties and biases in mobility analysis when big mobile data is used. The results from the project will also provide practical insights for practitioners in using big mobile data for mobility analysis.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.

这项NSF资助将量化与人类移动模式相关的偏差和不确定性，当它们来自手机数据，移动的应用程序数据和社交媒体数据等大移动的数据时。关于人类流动模式的信息，或者美国人在哪里以及如何生活、工作和进行日常活动的信息，是数千亿美元投资于国家交通基础设施的基础。这些投资决定直接影响到美国人的向上社会流动性、健康和福祉。该项目的动机有两个因素：第一，大移动的数据在流动性分析中越来越多地取代传统的家庭调查数据;第二，大移动的数据从根本上讲是不具代表性的（和有偏见的），直接应用这些数据得出的结果可能对美国人的健康，繁荣和福利产生重大负面影响。新颖的教育和外展活动与研究有机结合，包括与波士顿科学博物馆合作举办关于世界各地流动故事的数字展览，以及与MetroLab举办的关于“世界各地学生的未来流动性和正义”的迷你赛道竞赛。”除了量化与流动模式相关的偏差和不确定性外，该补助金还将确定这些偏差和不确定性受许多因素影响的程度，例如，数据特征、使用的建模技术和地理差异。更具体地说，该项目包括三个研究重点。Thrust 1与利益相关者和研究界合作，呼吁世界各地的移动实验室使用自己的数据和方法提交关键的移动指标。推力2涉及两种新方法的开发：耦合Bootstrap计算框架，以量化与导出的移动性指标和基于规则的学习框架相关的偏差和不确定性，以处理分析阶段可能出现的稀疏性问题。目标3涉及所有参与实验室的结果总结和传播。该项目将联合从交通工程到系统工程、计算机/信息科学和社会科学的多个学科，共同努力，更好地理解使用大移动的数据时移动分析中的不确定性和偏见。该项目的结果还将为从业者使用大移动的数据进行移动性分析提供实用的见解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。