III : Small : Integrating and Learning on Spatial Data via Multi-Agent Simulation

III：小：通过多智能体模拟集成和学习空间数据

• 批准号: 2311954
• 负责人: Jeff Phillips
• 金额: $ 60万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311954&HistoricalAwards=false
• 关键词: III; Small; Integrating; Learning; Spatial

The movement of humans throughout a city or region is critical to economic activity, social interactions, and infrastructure development. While some aspects of mobility can and have been studied at the microscopic level, and regional statistical properties could be gathered, the task of providing a holistic model for a global scale that adheres consistently with micro-scale dynamics has remained a challenge. For a full model of the economic and societal consequences, it is essential to gather socio-demographic data and to track movements across various modes of transportation and time scales. Pressing challenges, such as optimizing electric vehicle charging infrastructure and promoting equitable transportation law enforcement, that directly impact people’s daily lives, will benefit from access to such data and easily-usable models. Towards these goals, this project will build a foundational model for human mobility. This model can incorporate diverse types of movement, temporal, and socio-demographic data. It can also generate outputs relevant to a micro-scale event, or a macro-scale property, and consistent with both. At the core of this model is a simulation engine that can imitate events and movements in a way that is consistent with all of the data used as input. The learning of this representation will utilize modern machine learning techniques, and simultaneously optimize to align with different types of data modalities. The resulting simulation model leverages innovations in spatio-temporal anomaly detection to study intervention and prediction tasks. Ultimately these tangible and accessible models will help facilitate broadening participation in STEM by developing the next generation of spatial scientists through new joint programs between computing, data science, and civil 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.

人类在整个城市或地区的运动对于经济活动，社会互动和基础设施发展至关重要。尽管移动性的某些方面可以并且已经在微观层面上进行了研究，并且可以收集区域统计属性，但为全球量表提供整体模型的任务，该模型一致地遵守微尺度动力学，这仍然是一个挑战。对于经济和社会后果的完整模型，必须收集社会人口统计数据并跟踪各种运输方式和时间尺度的运动。紧迫的挑战，例如优化电动汽车充电基础设施和促进直接影响人们日常生活的公平运输执法，将受益于获取此类数据和易于使用的模型。为了实现这些目标，该项目将建立人类流动性的基础模型。该模型可以包含潜水员类型的运动，临时和社会人口统计学数据。它还可以生成与微尺度事件或宏观属性属性相关的输出，并且与两者一致。该模型的核心是模拟引擎，该引擎可以以与所有用作输入的数据一致的方式模仿事件和运动。该表示形式的学习将利用现代机器学习技术，并可以轻松优化以与不同类型的数据模式保持一致。所得的仿真模型利用时空异常检测中的创新来研究干预和预测任务。最终，这些有形且可访问的模型将通过计算，数据科学和土木工程之间的新联合计划来开发下一代空间科学家来帮助扩大STEM的参与。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。