Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
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 …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
An interdisciplinary survey on origin-destination flows modeling: Theory and techniques
Origin-destination (OD) flow modeling is an extensively researched subject across multiple
disciplines, such as the investigation of travel demand in transportation and spatial …
disciplines, such as the investigation of travel demand in transportation and spatial …
Pattern expansion and consolidation on evolving graphs for continual traffic prediction
Recently, spatiotemporal graph convolutional networks are becoming popular in the field of
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …
traffic flow prediction and significantly improve prediction accuracy. However, the majority of …
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Recent years have witnessed a thriving growth of computing facilities connected at the
network edge, cultivating edge networks as a fundamental infrastructure for supporting …
network edge, cultivating edge networks as a fundamental infrastructure for supporting …
Distributional and spatial-temporal robust representation learning for transportation activity recognition
Transportation activity recognition (TAR) provides valuable support for intelligent
transportation applications, such as urban transportation planning, driving behavior …
transportation applications, such as urban transportation planning, driving behavior …
Diff-rntraj: A structure-aware diffusion model for road network-constrained trajectory generation
Trajectory data is essential for various applications. However, publicly available trajectory
datasets remain limited in scale due to privacy concerns, which hinders the development of …
datasets remain limited in scale due to privacy concerns, which hinders the development of …
A graph and attentive multi-path convolutional network for traffic prediction
Traffic prediction is an important and yet highly challenging problem due to the complexity
and constantly changing nature of traffic systems. To address the challenges, we propose a …
and constantly changing nature of traffic systems. To address the challenges, we propose a …
Detecting extreme traffic events via a context augmented graph autoencoder
Accurate and timely detection of large events on urban transportation networks enables
informed mobility management. This work tackles the problem of extreme event detection on …
informed mobility management. This work tackles the problem of extreme event detection on …
A correlation information-based spatiotemporal network for traffic flow forecasting
Traffic flow forecasting technology plays an important role in intelligent transportation
systems. Based on graph neural networks and attention mechanisms, most previous works …
systems. Based on graph neural networks and attention mechanisms, most previous works …
Regularized Spatial–Temporal Graph Convolutional Networks for Metro Passenger Flow Prediction
One of the challenging topics in Intelligent Transportation Systems (ITSs) is the metro
passenger flow prediction. It has great practical significance for the daily crowd management …
passenger flow prediction. It has great practical significance for the daily crowd management …