Urban ride-hailing demand prediction with multiple spatio-temporal information fusion networkGuangyin Jin, Jincai Huang, Liang Zeng et al.|Transportation Research Part C Emerging Technologies|2020Cited by 145
Deep multi-view graph-based network for citywide ride-hailing demand predictionGuangyin Jin, Jincai Huang, Hengyu Sha et al.|Neurocomputing|2022Cited by 44
Urban Fire Situation Forecasting: Deep sequence learning with spatio-temporal dynamicsGuangyin Jin, Xingchen Hu, Cunchao Zhu et al.|Applied Soft Computing|2020Cited by 42
GSEN: An ensemble deep learning benchmark model for urban hotspots spatiotemporal predictionGuangyin Jin, Jincai Huang, Yanghe Feng et al.|Neurocomputing|2021Cited by 27
Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand PredictionGuangyin Jin, Jincai Huang, Hengyu Sha et al.|arXiv (Cornell University)|2020Cited by 15