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流动对于经济活动、社会互动和基础设施发展至关重要，虽然流动性的某些方面可以并且已经在微观层面上进行研究，并且可以收集区域统计特性，但提供与微观尺度动态一致的全球尺度整体模型仍然是一个挑战，对于经济和社会后果的完整模型来说，收集社会人口数据并跟踪各种交通方式和时间的运动至关重要。规模紧迫的挑战，例如优化电动汽车。直接影响人们日常生活的充电基础设施和促进公平的交通执法将受益于这些数据和易于使用的模型，为了实现这些目标，该项目将建立一个人类流动的基础模型。该模型可以包含多种类型。它还可以生成与微观尺度事件或宏观尺度属性相关的输出，并且该模型的核心是可以模仿事件的模拟引擎。和运动方式是一致的用作输入的所有数据将利用现代机器学习技术，并同时进行优化以与不同类型的数据模式保持一致，从而利用时空异常检测的创新来研究干预和预测任务。最终，这些有形且易于使用的模型将通过计算、数据科学和土木工程之间的新联合项目培养下一代空间科学家，从而有助于扩大对 STEM 的参与。该奖项反映了 NSF 的法定使命，并被认为值得通过评估获得支持使用基金会的智力价值和更广泛的影响审查标准。