Online reinforcement learning (RL) algorithms are increasingly used to personalize digital
interventions in the fields of mobile health and online education. Common challenges in …

Event cameras with their high temporal and dynamic range and minimal memory usage
have found applications in various fields. However their potential in static traffic monitoring …

The generation of trajectory data has increased dramatically with the advent and
widespread use of GPS-enabled devices. This rich source of data provides invaluable …

Simulating human mobility contributes to city behavior discovery and decision-making.
Although the sequence-based and image-based approaches have made impressive …

With an ever-increasing deployment density of probe and fixed sensors, massive vehicular
trajectory data is available and show a promising foundation to improve the observability of …

This paper presents a deep Inverse Reinforcement Learning (IRL) framework that can learn
an a priori unknown number of nonlinear reward functions from unlabeled experts' …

Modeling how human moves in the space is useful for policy-making in transportation, public
safety, and public health. The human movements can be viewed as a dynamic process that …

Traffic simulation is an important computational technique that models the behavior and
interactions of vehicles, pedestrians, and infrastructure in a transportation system …

Over the past three years, the COVID-19 pandemic has highlighted the importance of
understanding how people travel in contemporary urban areas in order to produce high …

How to perform human mobility simulation and generate massive and high-quality individual
mobility trajectories is an important and challenging research problem. Existing works …