SAI-P: Overcoming Barriers to User-Centered Infrastructure Planning with System Modeling and Natural Language Processing

加强美国基础设施（SAI）是NSF的一项计划，旨在促进以人为本的基础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改善生活质量奠定了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，促进经济增长，创造就业机会，提高公共部门服务提供的效率，加强社区建设，促进机会平等，保护自然环境，增强国家安全，并推动美国的领导地位。为了实现这些目标，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使建设和维护有效的基础设施，改善生活和社会，并建立在技术和工程的进步。加强这一基础设施是国家的一个高度优先事项。 这个SAI规划项目的重点是计算机技术如何改善交通规划，使昂贵的基础设施投资取得成功，并为社区的所有成员服务。这一领域的一个主要挑战是，许多交通项目（桥梁、高速公路、轻轨线路）都经历了社区的推后、成本超支、进度延误和漫长的审批过程。当这些项目完成时，结果有时会令人失望，因为结果不如预期的那么好。项目也可能对穷人、残疾人或其他边缘化群体产生负面影响。交通规划者可以通过尽早让专家和社区成员参与进来，向他们学习，并预测结果来避免这些问题。然而，在这样做的时候，规划团队可能会被太多关于不同关注点和观点的信息所淹没。该项目通过使用技术帮助规划人员更彻底地处理数据来解决这个问题。因此，交通项目将更好地满足用户需求，并为社区产生更积极的影响。该项目利用计算机技术的最新进展，帮助交通规划人员处理大量复杂的数据。该项目结合了两种技术：自然语言处理（NLP）和模糊认知映射（FCM）。NLP技术有助于文本的处理和解释（专家报告，社区评论，社交媒体上的帖子）。FCM帮助人们理解复杂情况下的因果关系。FCM在这个项目中用于帮助人们了解交通计划的可能结果。这些技术相结合，创造了一种新的交通规划方法。一个参与性框架将专家、社区成员和规划者聚集在一起，对新方法进行测试并提供反馈。该SAI规划活动开发和评估了参与式规划方法的技术基础，并设计了在现实世界规划情况下测试这种方法的计划，以确保其在实践中发挥作用。该奖项由社会，行为，经济（SBE）该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的学术价值和更广泛的影响审查标准。

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.The U.S. transportation infrastructure is aging rapidly. Strengthening this infrastructure is a high priority for the nation. This SAI planning project focuses on how computer technology can improve transportation planning so that costly investments in infrastructure are successful and serve all members of a community. A major challenge in this area is that many transportation projects (bridges, highways, light rail lines) experience community push back, cost overruns, schedule delays, and lengthy approval processes. When such projects are completed, the results can sometimes be disappointing because the outcomes are not as good as expected. Projects can also negatively impact people who are poor, disabled, or marginalized in other ways. Transportation planners can avoid such problems by involving experts and community members early, learning from them, and anticipating outcomes. In doing so, however, planning teams can get overwhelmed by too much information about diverse concerns and opinions. This project addresses the problem by using technology to help planners process data more thoroughly. As a result, transportation projects will better serve user needs and produce more positive impacts for communities.This project harnesses recent advances in computing technology to help transportation planners process large volumes of complex data. Two technologies are combined in this project: Natural Language Processing (NLP) and Fuzzy Cognitive Mapping (FCM). NLP technologies assist in the processing and interpretation of texts (expert reports, community comments, posts on social media). FCM helps people understand causes and effects in complex situations. FCM is used in this project to help people understand the likely outcomes of a transportation plan. These technologies are combined to create a new transportation planning approach. A participatory framework brings experts, community members, and planners together to test and provide feedback on the new approach. This SAI planning activity develops and evaluates the technical foundations of the participatory planning method and designs plans for testing this approach in real-world planning situations to ensure it works in practice.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Engineering.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.