Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

Recently, spatiotemporal graph convolutional networks are becoming popular in the field of
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …

Self-supervised representation learning (SSRL) concerns the problem of learning a useful
data representation without the requirement for labelled or annotated data. This …

Machine learning techniques are now integral to the advancement of intelligent urban
services, playing a crucial role in elevating the efficiency, sustainability, and livability of …

Traffic flow prediction is increasingly vital for the administration of metropolitan areas. Many
research on spatio-temporal networks have been explored but the impacts of both spatial …

As a critical mission of intelligent transportation systems, urban flow prediction (UFP)
benefits in many city services including trip planning, congestion control, and public safety …

Spatio-temporal prediction is crucial in numerous real-world applications, including traffic
forecasting and crime prediction, which aim to improve public transportation and safety …

Predicting urban flow is crucial for intelligent transportation systems (ITS), but it is not easy
due to several complicated elements (such as dynamic spatio-temporal dependencies …

Urban flow prediction is critical for urban planning, management, and safety. However,
owing to the inherent instability of urban flows, prediction accuracy requires the fusion of …