Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Crowd dynamics, particularly within multi-directional pedestrian flows, present a complex
system that has been a focal point in simulation studies due to its intricate complexity. In …

Generative self-supervised learning on graphs, particularly graph masked autoencoders,
has emerged as a popular learning paradigm and demonstrated its efficacy in handling non …

Urban traffic congestion significantly affects economic productivity, environmental
sustainability, and quality of life. Traditional traffic congestion prediction models, which are …

Crowd flow connects various geographic spaces in cities, revealing inter-regional
associations that are crucial for urban land–use identification. Existing research mainly …

Relational data, especially graphs where entities are represented as nodes and the
relations connecting them are denoted as edges, have become a common language for …

In order to achieve accurate prediction of short-term spatial and temporal distribution of
crowd density in each area after the crowd enters the station space, and to solve the …