Guangyin Jin

Orcid: 0000-0002-9837-6836

According to our database1, Guangyin Jin authored at least 27 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

On csauthors.net:

Bibliography

2023
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction.
IEEE Trans. Intell. Transp. Syst., August, 2023

Dual Graph Convolution Architecture Search for Travel Time Estimation.
ACM Trans. Intell. Syst. Technol., August, 2023

A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems.
Int. J. Gen. Syst., August, 2023

Urban hotspot forecasting via automated spatio-temporal information fusion.
Appl. Soft Comput., March, 2023

Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution.
ACM Trans. Knowl. Discov. Data, January, 2023

Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis.
CoRR, 2023

A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects.
CoRR, 2023

HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting.
CoRR, 2023

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey.
CoRR, 2023

Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Network-Wide Link Travel Time and Station Waiting Time Estimation Using Automatic Fare Collection Data: A Computational Graph Approach.
IEEE Trans. Intell. Transp. Syst., 2022

Adaptive Dual-View WaveNet for urban spatial-temporal event prediction.
Inf. Sci., 2022

Deep multi-view graph-based network for citywide ride-hailing demand prediction.
Neurocomputing, 2022

STGNN-TTE: Travel time estimation via spatial-temporal graph neural network.
Future Gener. Comput. Syst., 2022

Jointly Modeling Intersections and Road Segments for Travel Time Estimation via Dual Graph Convolutional Networks.
Proceedings of the Spatial Data and Intelligence - Third International Conference, 2022

Automated Spatio-Temporal Synchronous Modeling with Multiple Graphs for Traffic Prediction.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021
GSEN: An ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction.
Neurocomputing, 2021

Spatial-Temporal Dual Graph Neural Networks for Travel Time Estimation.
CoRR, 2021

Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution.
CoRR, 2021

A Deep Urban Hotspots Prediction Framework with Modeling Geography-Semantic Dynamics.
Proceedings of the Spatial Data and Intelligence - Second International Conference, 2021

Hierarchical Neural Architecture Search for Travel Time Estimation.
Proceedings of the SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems, 2021

2020
CSAN: A neural network benchmark model for crime forecasting in spatio-temporal scale.
Knowl. Based Syst., 2020

Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction.
CoRR, 2020

Urban Fire Situation Forecasting: Deep sequence learning with spatio-temporal dynamics.
Appl. Soft Comput., 2020

SDPN: A Neural Network Approach for E-hailing Car Supply and Demand Prediction.
Proceedings of the BDET 2020: 2nd International Conference on Big Data Engineering and Technology, 2020

2019
Crime-GAN: A Context-based Sequence Generative Network for Crime Forecasting with Adversarial Loss.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
Addressing the Task of Rocket Recycling with Deep Reinforcement Learning.
Proceedings of the 6th International Conference on Information Technology: IoT and Smart City, 2018


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