Abstract Intelligent Transportation System (ITS) is vital in improving traffic congestion,
reducing traffic accidents, optimizing urban planning, etc. However, due to the complexity of …

Intersections are among the most hazardous roadway spaces due to the complex and
conflicting road users' movements. Spatio-temporal modeling of conflict risks among road …

Predicting metro incident duration is crucial for passengers and transit operators to choose
appropriate response strategies. Most existing research focuses on structured data, the rich …

Practical applications of graph neural networks (GNNs) in transportation are still a niche
field. There exists a significant overlap between the potential of GNNs and the issues in …

Predicting incident duration and understanding incident types are essential in traffic
management for resource optimization and disruption minimization. Precise predictions …

Traffic flow anomaly detection is crucial for traffic management and reducing adverse
impacts. However, due to the lack of labeled information for anomaly events and the highly …

In the era of data‐driven transportation development, traffic forecasting is crucial.
Established studies either ignore the inherent spatial structure of the traffic network or ignore …

Spatiotemporal prediction over graphs (STPG) is challenging, because real-world data
suffers from the Out-of-Distribution (OOD) generalization problem, where test data follow …

As the complexities of urban environments continue to grow, the modelling of transportation
systems become increasingly challenging. This paper explores the application of advanced …

В современных городах вопросы экологии, связанные с ав-тотранспортом, занимают
всё более важное место в системе управления го-родскими транспортными потоками …