Smart cities result from integrating advanced technologies and intelligent sensors into
modern urban infrastructure. The Internet of Things (IoT) and data integration are pivotal in …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

Estimating the travel time of urban trajectories is a basic but challenging task in many
intelligent transportation systems, which is the foundation of route planning and traffic …

Accurate traffic prediction is a challenging task in intelligent transportation systems because
of the complex spatio-temporal dependencies in transportation networks. Many existing …

Crime event analysis (CEA) has become increasingly important in assisting humans in
preventing future crimes. A fundamental challenge in the research community lies in the …

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent
transportation system construction. Predictable ride-hailing demand can facilitate more …

Urban hotspot forecasting is one of the most important tasks for resource scheduling and
security in future smart cities. Most previous works employed fixed neural architectures …

Spatial–temporal event prediction is a particular task for multivariate time series forecasting.
Therefore, the complex entangled dynamics of space and time need to be considered. This …

Deep learning approaches, given their low cost and high reliability, have gained much
popularity in different subjects, such as computer vision and natural language processing …

Travel time estimation is one of the core tasks for the development of intelligent
transportation systems. Most previous works model the road segments or intersections …