Projects

Abstract: The integration of generative AI and large language models (LLMs) in academia enhances access and collaboration but also raises concerns about misinformation and academic integrity. Analyzing 80 academic guidelines, we recommend balanced approaches for their responsible use in education. The cover image is sourced from Pexels and is free… read more 

Abstract: Public EV charging stations (EVCSs) are essential for EV adoption. This study analyzes Seoul’s public EV charging patterns in relation to the urban built environment. Built-environment data were collected from land-use maps, POI data, and panorama images, with computer vision extracting scene features. A spatiotemporal analysis revealed peak afternoon… read more 

Abstract: This study explores disparities in Electric Vehicle (EV) adoption across different socio-economic groups and geographic areas in Texas, focusing on the Texas Triangle (Austin, Houston, San Antonio, and Dallas-Fort Worth). Using EV registration data and hierarchical clustering, the research identified distinct adoption patterns. Tesla emerged as the dominant brand,… read more 

Abstract: This paper introduces AI-FEED, a web-based platform (ai-feed.ai) designed to tackle food and nutrition insecurity within the food charity ecosystem. Using AI and blockchain technology, AI-FEED improves access to nutritious food and optimizes resource allocation, aiming to reduce food waste and enhance community health. It was developed through stakeholder… read more 

Abstract: Transit deserts are areas where transit demand exceeds supply. This study not only identifies these regions but also proposes actionable solutions using a multi-class supervised machine learning framework. Focusing on peak times and gender differences in demand, the study evaluated several models and selected the Random Forest method. Key… read more 

Abstract: Transportation electrification is promoted for its environmental benefits, but EV adoption shows complex patterns influenced by race and income disparities. Recent studies often overlook regional ownership variations and urban form measures. This study uses actual EV registration data with spatial analyses, revealing an East–West divide in Austin. West Austin… read more 

Abstract: This study investigates the patterns of extreme heat during summers and their link to heat-related Emergency Medical Services (EMS) incidents in Austin-Travis County, Texas, focusing on 2020 and 2021. Analyzing 47,838 heat-related EMS cases, the research identifies significant correlations between high heat index (HI) frequency/intensity and increased EMS incidents,… read more 

Abstract:  This study identifies fire vulnerability patterns, maps high-risk areas with limited access to fire stations and hospitals, and determines factors contributing to increased fire incidents. A fire vulnerability index was developed using health and socio-environmental factors, while accessibility to resources was assessed through E2SFCA analysis. OLS and GWR regression… read more 

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 

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