Convergence Accelerator Phase I(RAISE): Network for Equity in the Era of Driverless Vehicles

融合加速器第一阶段（RAISE）：无人驾驶汽车时代的公平网络

• 批准号: 1936884
• 负责人: Robert Hampshire
• 金额: $ 94.82万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936884&HistoricalAwards=false
• 关键词: Convergence; Accelerator; Phase; RAISE; Network

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to identify how major recent transportation innovations, including ridesourcing and driverless vehicles, can improve economic prosperity and the quality of life for all Americans. Citizens and policy makers need knowledge-based, data-driven responses to these massive disruptions of the transportation sector in order for the U.S. to be at the forefront of technological advances and to be able to provide equitable access to transportation. We propose to create an open knowledge network (OKN) for transportation and the emerging mobility ecosystem that leverages and links existing publicly available data (i.e. www.data.gov). To do this, we will integrate perspectives and insights from engineering, computer science, statistics, social and behavioral science, systems science and public policy. Our transdisciplinary, convergence team is a partnership between public, private and the not-for-profit sectors. The resulting knowledge network will result in better public policy decisions on the deployment of driverless vehicles, sustainable urban planning practices, and more equitable transportation systems design. Data deliverables from the project will empower our project partners and end users to develop innovation ecosystems specific to their local needs and populations of interest. With the deployment of innovative transportation technologies such as driverless vehicles on the horizon, we need a more thorough and broader understanding of their potential economic and quality of life impacts. Our project speaks directly to this need by creating an open knowledge network to accelerate these innovations while being aware of their broader implications on society. Our research will link publicly available microdata and scientific inquiries on the impacts of new mobility modes. The objective is to gain deeper insights into the factors that prevent the development of transportation innovations but also to understand the ways in which transportation innovations contribute to societal well-being. Our proposed research addresses a myriad of factors such as ontology development, entity matching, synthetic entity generation, data normalization and linking strategies. The major tasks are 1) conducting a customer discovery process; 2) building a transdisciplinary convergence team; 3) developing a robust OKN research plan and 4) the creation of a minimum viable knowledge network (MVKN). The MVKN is expected to provide crucial insights, particularly at city or metropolitan level, to decision makers in the transportation arena.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.

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。 这个融合加速器第一阶段项目的更广泛的影响和潜在的社会效益是确定最近的主要交通创新，包括乘车和无人驾驶汽车，如何改善经济繁荣和所有美国人的生活质量。公民和政策制定者需要以知识为基础，以数据为驱动的应对交通部门的这些大规模中断，以便美国处于技术进步的最前沿，并能够提供公平的交通服务。我们建议为交通和新兴的移动生态系统创建一个开放知识网络（OKN），该网络利用并链接现有的公开数据（即www.example.com）。为此，我们将整合工程，计算机科学，统计学，社会和行为科学，系统科学和公共政策的观点和见解。我们的跨学科融合团队是公共，私人和非营利部门之间的合作伙伴关系。由此产生的知识网络将导致更好的公共政策决策，包括部署无人驾驶车辆、可持续城市规划实践和更公平的交通系统设计。该项目的数据交付将使我们的项目合作伙伴和最终用户能够开发针对当地需求和感兴趣人群的创新生态系统。 随着无人驾驶汽车等创新交通技术的部署，我们需要对其潜在的经济和生活质量影响进行更全面、更广泛的了解。我们的项目通过创建一个开放的知识网络来加速这些创新，同时意识到它们对社会的更广泛影响，从而直接满足了这一需求。 我们的研究将把公开的微观数据和科学调查联系起来，以了解新的移动模式的影响。其目的是更深入地了解阻碍交通创新发展的因素，同时了解交通创新如何促进社会福祉。我们提出的研究解决了无数的因素，如本体开发，实体匹配，合成实体生成，数据规范化和链接策略。主要任务是：1）进行客户发现过程; 2）建立跨学科融合团队; 3）制定强大的OKN研究计划; 4）创建最小可行知识网络（MVKN）。MVKN有望为交通竞技场的决策者提供重要的见解，特别是在城市或大都市一级。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。