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

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“融合加速器”第一阶段项目的更广泛影响和潜在社会效益是，确定最近主要的交通创新，包括拼车和无人驾驶汽车，如何促进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，以数据为导向的应对交通运输部门的这些大规模中断，以便美国站在技术进步的最前沿，并能够提供公平的交通运输。我们建议为交通和新兴的移动生态系统创建一个开放的知识网络（OKN），利用和链接现有的公共数据（例如www.data.gov）。为此，我们将整合来自工程学、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、私营和非营利部门之间的伙伴关系。由此产生的知识网络将在部署无人驾驶汽车、可持续城市规划实践和更公平的交通系统设计方面做出更好的公共政策决策。项目交付的数据将使我们的项目合作伙伴和最终用户能够根据当地需求和感兴趣的人群开发创新生态系统。随着无人驾驶汽车等创新交通技术的部署，我们需要更全面、更广泛地了解它们对经济和生活质量的潜在影响。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，直接满足了这一需求。我们的研究将把公开的微数据和关于新移动模式影响的科学调查联系起来。目标是深入了解阻碍交通创新发展的因素，同时也了解交通创新对社会福祉的贡献方式。我们提出的研究涉及许多因素，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)构建跨学科融合团队；3)制定健全的OKN研究计划和4)创建最小可行知识网络（MVKN）。MVKN预计将为交通领域的决策者提供重要的见解，特别是在城市或大都市层面。该奖项反映了美国国家科学基金会的法定使命，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。

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.