61 citations · 168 across the 9 of their papers we have counts for
9 papers
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
Guangyin Jin, Lingbo Liu, Fuxian Li +1
Traffic congestion event prediction is an important yet challenging task in intelligent transportation systems. Many existing works about traffic prediction integrate various tempo…
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Zezhi Shao, Fei Wang, Tao Sun +7
Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential compone…
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang +4
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-tempora…
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
Guangyin Jin, Fuxian Li, Jinlei Zhang +2
Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existin…
STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems
Jinlei Zhang, Hua Li, Lixing Yang +3
Short-term passenger flow prediction is an important but challenging task for better managing urban rail transit (URT) systems. Some emerging deep learning models provide good insi…
Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach
Jinlei Zhang, Feng Chen, Lixing Yang +3
Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for…
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Fuxian Li, Jie Feng, Huan Yan +3
Traffic prediction is the cornerstone of an intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent tra…
Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation
Guangyin Jin, Huan Yan, Fuxian Li +2
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 separately b…
Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction
Guangyin Jin, Zhexu Xi, Hengyu Sha +2
Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reaso…