| Privacy-preserving traffic flow prediction: A federated learning approach Y Liu, JQ James, J Kang, D Niyato, S Zhang IEEE Internet of Things Journal 7 (8), 7751-7763, 2020 | 521 | 2020 |
| FASTGNN: A topological information protected federated learning approach for traffic speed forecasting C Zhang, S Zhang, JQ James, S Yu IEEE Transactions on Industrial Informatics 17 (12), 8464-8474, 2021 | 108 | 2021 |
| Fedgru: Privacy-preserving traffic flow prediction via federated learning Y Liu, S Zhang, C Zhang, JQ James 2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020 | 30 | 2020 |
| Robust federated learning approach for travel mode identification from non-IID GPS trajectories Y Zhu, S Zhang, Y Liu, D Niyato, JQ James 2020 IEEE 26th International Conference on Parallel and Distributed Systems …, 2020 | 26 | 2020 |
| Traffic data imputation with ensemble convolutional autoencoder Y Ye, S Zhang, JQ James 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 13 | 2021 |
| An enhanced motif graph clustering-based deep learning approach for traffic forecasting C Zhang, S Zhang, JQ James, S Yu GLOBECOM 2020-2020 IEEE Global Communications Conference, 1-6, 2020 | 13 | 2020 |
| Attention-driven recurrent imputation for traffic speed S Zhang, C Zhang, S Zhang, JQ James IEEE Open Journal of Intelligent Transportation Systems 3, 723-737, 2022 | 7 | 2022 |
| Toward large-scale graph-based traffic forecasting: A data-driven network partitioning approach C Zhang, S Zhang, X Zou, S Yu, JQ James IEEE Internet of Things Journal 10 (5), 4506-4519, 2022 | 6 | 2022 |
| ST-AGNet: Dynamic power system state prediction with spatial–temporal attention graph-based network S Zhang, S Zhang, JQ James, X Wei Applied Energy 365, 123252, 2024 | | 2024 |
James Jianqiao Yu 余剑峤University of York在 york.ac.uk 的电子邮件经过验证
