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: 2114197
• 负责人: Qi Wang
• 金额: $ 17.69万
• 依托单位: Northeastern University
• 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 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114197&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.

美国国家科学基金会的这笔拨款将量化与人类流动模式相关的偏差和不确定性，这些偏差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有关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。这些投资决策直接影响美国人的社会流动性、健康和福祉。 ：第一，移动大数据日益取代传统流动性分析中的家庭调查数据；其次，大移动数据根本上不具有代表性（并且有偏见），直接应用从这些数据中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，包括与波士顿科学博物馆合作举办关于世界各地流动性故事的数字展览，以及与 MetroLab 合作举办“未来流动性和世界各地学生的正义”的迷你赛道竞赛。量化相关的偏差和不确定性对于流动模式，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，例如具体的数据特征、所使用的建模技术和地理差异。此外，该项目包括三个研究重点。 Thrust 2 涉及开发两种新颖的方法：耦合的 Bootstrap 计算框架，用于量化偏差和偏差。 Thrust 3 涉及与导出的移动指标相关的不确定性和基于规则的学习框架，以处理分析阶段可能出现的稀疏性问题，该项目将联合从运输工程到系统工程的多个学科。该项目的结果也将为从业者使用移动大数据进行移动分析提供实用见解。本次获奖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，NSF 的法定使命被认为值得支持。