In recent years, artificial intelligence (AI) has been increasingly put into use to address cities'
economic, social, environmental, and governance challenges. Thanks to its advanced …

Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

Spatio-temporal prediction aims to forecast and gain insights into the ever-changing
dynamics of urban environments across both time and space. Its purpose is to anticipate …

We provide a brief review of human mobility science and present three key areas where we
expect to see substantial advancements. We start from the mind and discuss the need to …

Pervasive integration of GPS-enabled devices and data acquisition technologies has led to
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …

Abstract Non-Pharmaceutical Interventions (NPIs), aimed at reducing the diffusion of the
COVID-19 pandemic, have dramatically influenced our everyday behaviour. In this work, we …

The last decade has witnessed the emergence of massive mobility datasets, such as tracks
generated by GPS devices, call detail records, and geo-tagged posts from social media …

Dockless bike-sharing is an effective solution for the metro's first-and last-mile connections.
To create a more bicycle-friendly environment, there is a need to accurately predict the use …

Generating trajectory data is among promising solutions to addressing privacy concerns,
collection costs, and proprietary restrictions usually associated with human mobility …