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

University Of Utah, Salt Lake City UT

Investigators

Abstract

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.