EAGER: SAI: Cognitive Models of Human Social Wayfinding for the Redesign of Public Spaces

加强美国基础设施(SAI)是一项NSF计划，旨在刺激以人类为中心的基础性研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，增长经济，创造就业机会，提高公共部门服务提供效率，加强社区力量，促进机会均等，保护自然环境，增强国家安全，推动美国的领导地位。要实现这些目标，需要来自科学和工程学科的专业知识。SAI专注于人类推理和决策、治理以及社会和文化过程的知识如何能够建设和维护有效的基础设施，从而改善生活和社会，并建立在技术和工程进步的基础上。2020年，许多公共空间被仓促重新设计，以优化行人流量，以最大限度地减少新冠肺炎的传播。不幸的是，模拟人们如何在公共场所移动的传统方法没有考虑到影响人们在他人面前实际导航的社会因素(社交寻路)。例如，这些方法不包括人们如何调整以避开他人的私人空间，在行动较慢的人周围导航，或遵循其他人的指示。更糟糕的是，现有的模拟通常假设每个人都有相同的能力，但在真实人群中很少出现这种情况。该项目的目标是开发一种系统，用于模拟公共空间中的人流，包括人类导航的社会方面，并纳入各种能力和残疾的人。这些更逼真的模拟将被用来开发用于评估公共空间的新指标和协议，这些标准和协议更全面地反映现实中人的丰富社交行为。该项目开发了一个新的公共空间人流建模框架，称为社交寻路启发基础设施(SWIIFT)设计框架。该框架由三个相互关联的部分组成：人体认路实验、公共空间人流的计算模拟以及用于评估真实公共空间设计和重新设计的评估指标。在一系列实验中，受试者将通过虚拟现实耳机沉浸在模拟空间中。这些空间将包含不同数量的模拟人，包括行动不便(使用轮椅、拐杖或助行器；推婴儿车；背沉重的袋子)、感觉能力(例如视觉障碍、听力障碍)、知识和注意力不同的人。人类受试者将收到关于走哪条路的不同提示，包括可见的路径、标志和口头指示。关于他们在虚拟空间中导航时所做选择的数据将被合并到模拟中，使我们能够开发出人们在自然条件下如何在空间中移动的现实模型。最后，这个框架将使用这些模拟模型来评估对真实空间的潜在修改，从而允许在实施之前准确地评估潜在昂贵的修改。这项工作的最终目标是使公共空间变得更有效率，更容易为每个人提供，无论其能力如何。这一奖项反映了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。

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.In 2020, many public spaces were hastily redesigned to optimize pedestrian flow in order to minimize the spread of COVID-19. Unfortunately, conventional methods for simulating how people move through public spaces do not take into account social factors that affect how people actually navigate in the presence of other people (social wayfinding). For example, these methods do not incorporate how people adjust to avoid others’ personal space, navigate around slower-moving people, or follow instructions from other people. Even worse, existing simulations usually assume everybody has identical abilities, which is rarely true in real populations. The goal of this project is to develop a system for simulating the flow of people through public spaces, including social aspects of human navigation, and incorporating people with a variety of abilities and disabilities. These more realistic simulations will be used to develop novel metrics and protocols for evaluating public spaces, which more thoroughly reflect the rich social behavior of real people. This project develops a new framework for modeling the flow of people through public spaces, called the Social Wayfinding-Inspired InFrasTructure (SWIIFT) design framework. The framework has three interlocking parts: human subjects experiments on human wayfinding, computational simulations of the flow of people through public spaces, and evaluation metrics for assessing design and re-design of real public spaces. In a series of experiments, human subjects will be immersed via Virtual Reality headsets into simulated spaces. These spaces will contain different numbers of simulated people, including people with variations in mobility (using wheelchairs, canes or walkers; pushing strollers; carrying heavy bags), sensory ability (e.g., visual impairments, hearing impairments), knowledge, and attention. Human subjects will receive different cues about which way to go, including visible pathways, signage, and verbal instructions. Data about the choices they make as they navigate through the virtual spaces will be incorporated into simulations, allowing us to develop realistic models of how people move through spaces under natural conditions. Finally, this framework will use these simulation models to evaluate potential modifications to real spaces, allowing potentially expensive changes to be accurately evaluated before they are carried out. The ultimate goal of this work is to enable public spaces to be made more efficient and more accessible for everyone, regardless of ability.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.