UT Urban Information Lab

Abstract: Real-time traffic data is crucial for adaptive traffic light control systems. Traditional sensors like infrared radiation and GPS lack detail. Surveillance cameras offer potential for detailed traffic analysis. This study utilizes a You Only Look Once (YOLO) algorithm for vehicle detection and tracking in traffic videos, aided by a… read more 

Abstract: Estimating health outcomes at a neighborhood level is crucial for urban health promotion but can be resource-intensive. This paper introduces a machine learning approach to predict the prevalence of six common chronic diseases at the census tract level in Austin, Texas. By experimenting with eight machine learning algorithms and… read more 

Abstract: This study investigates the impact of Airbnb listings on land values in the Austin, Texas area, focusing on single-family homes. Using three models—ordinary least squares regression, geographically weighted regression (GWR), and Bayesian analysis—it examines spatial distribution and temporal effects on land parcel data within Travis County. Results suggest that… read more 

Abstract: This study examines the impact of COVID-19 on housing prices in four major metropolitan areas in Texas: Austin, Dallas, Houston, and San Antonio. Using a linear mixed effects model, it analyzes socioeconomic, housing, and transportation factors affecting median home prices while considering fixed and random effects. Results reveal that… read more 

Abstract: This study examines how street patterns, metro stations, and urban function density influence pedestrian distribution in Tianjin, China. Thirteen neighborhoods from the city center and suburbs were selected for observation. Data on pedestrian and vehicle volumes were collected from 703 street segments. Regression models were employed to analyze the… read more 

Abstract: The COVID-19 crisis has transformed the importance of smart cities, highlighting the vital role of Information and Communications Technology (ICT) in crisis management and post-pandemic life. Originally a branding strategy, smart city technologies are now essential infrastructure facilitating remote work and online interactions. This urgency has prompted urban planners… read more 

Abstract: Traffic forecasting is vital for urban planning, with deep learning methods excelling in capturing traffic patterns. However, obtaining comprehensive historical data remains challenging, especially for city-wide predictions. To overcome this, we used SceneGCN, a deep learning approach, for city-scale traffic speed forecasting. This method involves extracting scene features from… read more 

Abstract: This study examined three AI image synthesis models—Dall-E 2, Stable Diffusion, and Midjourney—for generating urban design imagery from scene descriptions. 240 images were evaluated using a modified Sensibleness and Specificity Average (SSA) metric by two independent evaluators. Results revealed significant differences among the AI models, with varying scores across… read more 

Abstract: This study investigates the impact of technology-based corporation relocations on housing prices during COVID-19 in Austin, Texas, and Seattle/Bellevue, Washington, focusing on Tesla and Amazon. Using a difference-in-difference (DID) method, changes in housing prices near and away from the new corporate locations are analyzed within 5-mile and 10-mile radii.… read more