Urban transportation data are crucial for smart city development, enhancing traffic
management's intelligence, accuracy, and efficiency. This paper conducts a comprehensive …

Users may click on a news because they are interested in its content or because the news
contains important information and is very popular. Modeling these two aspects is crucial for …

Traffic flow prediction forms a critical foundation for the management and planning of urban
transportation systems. However, the complex spatial interactions among road segments …

Abstract Neural Ordinary Differential Equations (Neural ODEs), as a novel category of
modeling big data methods, cleverly link traditional neural networks and dynamical systems …

Cognitive networks with the integration of smart and physical devices are rapidly utilized for
the development of smart cities. They are explored by many real-time applications such as …

Session-based recommendation (SBR) predicts the next interaction of users based on their
clicked items in a session. Previous studies have shown that hypergraphs are superior in …

Trajectory similarity computing enables us to gain deeper insights into the movement
patterns of objects, which benefits intelligent transportation system applications from urban …

Abstract The High Utility Itemset mining (HUIM) is an important research area in the field of
data mining and knowledge discovery. HUIM aims to discover the high utility patterns from a …

Cycle-based arrival profiles can describe temporal demand distribution within a signal cycle
for signalized intersections, which can be used to calculate indicators such as traffic volume …

With the rapid advancements in artificial intelligence and smart mobility technologies, traffic
monitoring systems are evolving quickly. Among these systems, in-vehicle monitoring …