Imagine a city that can predict traffic jams before they happen, optimize energy use based on real-time weather data and even warn residents about how a nearby fire might spread. What once sounded futuristic is now on the horizon thanks in part to researchers from “A Good System for Smart Cities,”… read more
UIL at 2024 Johns Hopkins Center for Climate-Smart Transportation meeting
Dr. Kijin Seong was introducing the Urban Info Lab’ resources to the guests.
Yefu Chen, a member of the Urban Info Lab, officially received a PhD in May 2024
Congratulations to Yefu Chen, a member of the Urban Info Lab, recently received a PhD in Community and Regional Planning from The University of Texas at Austin.
2nd Annual Smart Cities and AI Innovations Symposium
About the Event The Smart Cities and AI Innovations Symposium is a day-long gathering of interdisciplinary professionals across academia and industry to inform, examine, and discuss how generative artificial intelligence impacts our cities. Happening in the Avaya Auditorium on the University of Texas at Austin main campus, this robust program will feature… read more
Spatio-temporal patterns of heat index and heat-related Emergency Medical Services (EMS)
Abstract: Research on summertime extreme heat patterns and their link to heat-related Emergency Medical Services (EMS) incidents remains limited, despite rising concerns about heat waves and their health impacts. This study explores spatiotemporal patterns of the heat index (HI) and its relationship to heat-related EMS incidents in Austin-Travis County, Texas,… read more
Exploring Urban Space through AI: Comparative Insights from OpenAI ChatGPT and Google Bard
Abstract: This study compares the abilities of two AI chatbots, OpenAI ChatGPT and Google Bard, to understand urban environments, inspired by Kevin Lynch’s “Image of the City.” We examined their grasp of city landmarks, navigation, and spatial perception. ChatGPT offered factual, generalized responses, while Google Bard provided detailed, immersive answers… read more
Towards AI-Generated Sustainable Cities: An Investigation of GPT-4 and DALL-E in Urban Design
Abstract: This study investigates the potential of AI tools, GPT-4 and DALL-E, in generating sustainable urban design concepts addressing key issues like urban heat islands, walkability, and green spaces. Through a systematic methodology, the study demonstrates the ability of these tools to generate novel ideas, while also revealing limitations in… read more
Comparing the impacts of COVID-19 on residential rental market across rental sectors: Evidence from city of Austin
Abstract: The COVID-19 pandemic has affected rental housing prices, but previous research lacks comprehensive data across different residential categories. This study fills that gap by analyzing 48 months of closed rental listings from the Austin region’s Multiple Listing Service. It examines the pandemic’s impact on four types of residential properties,… read more
EVALUATING THE AFFORDABILITY AND THE IMPACTS OF PUBLIC TRANSIT SERVICES: An Expandable and Predictable Framework of Location Affordability in Los Angeles County
Abstract: This dissertation addresses limitations in existing affordability frameworks, proposing an expandable framework to incorporate critical components like health costs. Focusing on Los Angeles County, it examines the impacts of public transit on location affordability, emphasizing accessibility factors and socio-demographic influences on property values. The study validates public transit’s role… read more
Identifying transit deserts for low-income commuters in Wuhan Metropolitan Area, China
Abstract: This research examines the spatial patterns of transit systems and commuter flows in the Wuhan Metropolitan Area, China, using Baidu users’ location data. It identifies transit deserts that particularly affect low-income commuters. The study reveals several key findings: Firstly, most transit demand originates from trips between neighboring communities, while… read more
