Abstract: This paper aims to investigate the following research questions: (1) what are the hourly patterns of heat index and heat-related emergency medical service (EMS) incidents during summertime?; and (2) how do the lagged effects of heat intensity and hourly excess heat (HEH) vary by heat-related symptoms? Using the hourly… read more
Projects
Effects of urban environmental factors on heat-related emergency medical services (EMS) response time
Abstract: Due to the time-sensitive nature of heat-related illnesses, disparities in access to heat-related emergency medical services (EMS) services may contribute to urban health disparities. This paper is an empirical study utilizing Austin-Travis County EMS data to estimate the delays in response time due to traffic congestion through spatiotemporal analysis… read more
Is a Smart City Framework the Key to Disaster Resilience? A Systematic Review
Abstract Despite a growing body of research on the smart city framework for disaster resilience, a comprehensive systematic literature review from urban planning perspectives has never been attempted. In this review of smart and resilient cities, we distill vital learning and shared concepts, identify research trends and limitations, and suggest… read more
Evaluating the effects of heat vulnerability on heat-related emergency medical service incidents: Lessons from Austin, Texas
Abstract Extreme heat exposure and sensitivity have been a growing concern in urban regions as the effects of extreme heat pose a threat to public health, the water supply, and the infrastructure. Heatrelated illnesses demand an immediate Emergency Medical Service (EMS) response since they might result in death or serious… read more
Fire and Smoke Digital Twin – A computational framework for modeling fire incident outcomes
ABSTRACT Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths… read more
Evaluating Air Quality Status in Chicago: Application of Street View Imagery and Urban Climate Sensors
Abstract Cities worldwide have initiated the installation of urban climate sensors to monitor air quality in real time and take proactive measures against the growing threat of climate change. This study focuses on the city of Chicago and utilizes Microsoft’s recently launched Project Eclipse sensors to evaluate air quality status.… read more
Combatting the mismatch: Modeling bike-sharing rental and return machine learning classification forecast in Seoul, South Korea
Abstract: Bike-sharing is rapidly gaining popularity due to health, transportation, and recreational benefits. As more people use bike-sharing, the burden of reallocating bikes will increase because of the mismatch between outgoing and incoming bikes. Optimizing truck routes, incentivizing users, and crowdsourcing are common suggestions to mitigate rebalancing issues. This research… read more
Impacts of COVID-19 on bike-sharing usages in Seoul, South Korea
Abstract: The COVID-19 pandemic and social distancing restrictions have had a significant impact on urban mobility. As micro mobility offers less contact with other people, docked or dockless e-scooters and bike-sharing have emerged as alternative urban mobility solutions. However, little empirical research has been conducted to investigate how COVID-19 might… read more
Understanding E-Scooter Incidents Patterns in Street Network Perspective: A Case Study of Travis County, Texas
Abstract: Dockless electric scooter (E-scooters) services have emerged in the United States as an alternative form of micro transit in the past few years. With the increasing popularity of E-scooters, it is important for cities to manage their usage to create and maintain safe urban environments. However, E-scooter safety in… read more
Tracking Property Ownership Variance and Forecasting Housing Price with Machine Learning and Deep Learning
Abstract— Big data and its production, management, and utilization are essential components in smart city planning. This paper presents a research framework for applying machine learning and deep learning using multiple big data sets on real estate. We built ensemble machine learning models to track property ownership variance in Austin,… read more