Although highly valuable for a variety of applications, urban mobility data are rarely made
openly available, as it contains sensitive personal information. Synthetic data aims to solve …

Health institutions and hospitals are essential in ensuring the appropriate treatment of
human health. One of the major concerns is the increasingly overcrowded patients care …

Background In 2020, COVID-19 has claimed more than 300,000 deaths in the United States
alone. Although nonpharmaceutical interventions were implemented by federal and state …

With the onset of COVID-19 and the resulting shelter in place guidelines combined with
remote working practices, human mobility in 2020 has been dramatically impacted. Existing …

Understanding, analyzing, and predicting human mobility and dynamics are valuable to
solving pressing problems, develo** effective plans, and prescribing timely remedies. As …

Urban mobility is a complex system influenced by various factors such as infrastructure,
technology, and human behavior. Agent-based modeling (ABM) has emerged as a valuable …

The aim of this work is to develop closer connectivity between real, lived human encounters
in everyday life and geosimulation that might be tasked and designed to experiment with …

The spread of infectious diseases is a highly complex spatiotemporal process, difficult to
understand, predict, and effectively respond to. Machine learning and artificial intelligence …

The COVID-19 pandemic showed us the importance of modeling and forecasting efforts to
guide decision makers. However, a year into the COVID-19 pandemic, the computational …

Foot traffic is a business term to describe the number of customers that enter a point of
interest (POI). This work aims to predict future foot traffic: the number of people from each …