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

NSF的这笔拨款将量化与人类移动模式相关的偏见和不确定性，这些偏见和不确定性来自手机数据、移动应用程序数据和社交媒体数据等大数据。有关人类流动模式的信息，或美国人在哪里以及如何生活、工作和日常活动的信息，是美国数千亿美元交通基础设施投资的基础。这些投资决定直接影响到美国人向上的社会流动性、健康和福祉。该项目的动机有两个：第一，在流动性分析中，大移动数据越来越多地取代传统的家庭调查数据；第二，大移动数据从根本上没有代表性(和偏见)，直接应用此类数据得出的结果可能会对美国人的健康、繁荣和福利产生重大负面影响。与研究有机结合的新颖的教育和推广活动，包括与波士顿科学博物馆合作，在世界各地举办关于流动性故事的数字展览，以及与MetroLab合作举办一场迷你赛道比赛，主题是“世界各地学生未来的流动性和正义”。除了量化与流动性模式相关的偏差和不确定性外，这笔赠款还将确定这些偏差和不确定性受多种因素影响的程度，例如数据特征、所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推动力。Struts 1让利益相关者和研究社区制定了一份邀请书，呼吁世界各地的移动性实验室使用自己的数据和方法提交关键的移动性指标。推力2涉及开发两种新的方法：用于量化与派生的流动性指标相关的偏差和不确定性的耦合Bootstrap计算框架，以及用于处理分析阶段可能出现的稀疏性问题的基于规则的学习框架。推力3涉及所有参与的实验室，以总结和传播成果。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数据时移动性分析中的不确定性和偏差。该项目的结果还将为实践者使用大数据进行移动性分析提供实践见解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.