AI technologies can help build smarter cities by using data to improve services, such as public safety, transit systems, and emergency response. They can also help us evaluate how new infrastructure, such as public transportation lines, will affect access to housing, jobs, and public services. However, the datasets used to create smart cities are large, messy, and fragmented across different domains such as mobility, housing, or energy. This project seeks to build a system that will link city datasets — extracting useful information, identifying any data bias, and ensuring information is used responsibly — to predict the effects of urban development projects, including Austin’s Project Connect.
Junfeng Jiao (Lead, Architecture), Ngoc Tran (Co-Lead, Math), Weijia Xu (Co-Lead, Texas Advanced Computing Center), Arya Farahi (Statistics and Data Science), Chandra Bhat (Civil, Architectural and Environmental Engineering), Devrim Ikizler (Economics), Dev Niyogi (Geosciences), Andrew Waxman (LBJ School of Public Affairs), Ming Zhang (Architecture), Mingyuan Zhou (McCombs School of Business), Hao Zhu (Electrical and Computer Engineering), Hannah Barron (Austin Transportation Department); Jason JonMichael (Austin Transportation Department), Alex Payson (Austin Transportation Department)