| A hybrid deep learning based traffic flow prediction method and its understanding Y Wu, H Tan, L Qin, B Ran, Z Jiang Transportation Research Part C: Emerging Technologies 90, 166-180, 2018 | 523 | 2018 |
| Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework Y Wu, H Tan arXiv preprint arXiv:1612.01022, 2016 | 298 | 2016 |
| Short-term traffic prediction based on dynamic tensor completion H Tan, Y Wu, B Shen, PJ Jin, B Ran IEEE Transactions on Intelligent Transportation Systems 17 (8), 2123-2133, 2016 | 181 | 2016 |
| Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus Y Wu, H Tan, J Peng, H Zhang, H He Applied energy 247, 454-466, 2019 | 176 | 2019 |
| Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle R Lian, J Peng, Y Wu, H Tan, H Zhang Energy 197, 117297, 2020 | 121 | 2020 |
| Tensor based missing traffic data completion with spatial–temporal correlation B Ran, H Tan, Y Wu, PJ Jin Physica A: Statistical Mechanics and its Applications 446, 54-63, 2016 | 116 | 2016 |
| Energy management of hybrid electric bus based on deep reinforcement learning in continuous state and action space H Tan, H Zhang, J Peng, Z Jiang, Y Wu Energy Conversion and Management 195, 548-560, 2019 | 92 | 2019 |
| Cross-type transfer for deep reinforcement learning based hybrid electric vehicle energy management R Lian, H Tan, J Peng, Q Li, Y Wu IEEE Transactions on Vehicular Technology 69 (8), 8367-8380, 2020 | 53 | 2020 |
| A comparison of traffic flow prediction methods based on DBN H Tan, X Xuan, Y Wu, Z Zhong, B Ran CICTP 2016, 273-283, 2016 | 48 | 2016 |
| Inductive graph neural networks for spatiotemporal kriging Y Wu, D Zhuang, A Labbe, L Sun Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4478-4485, 2021 | 46 | 2021 |
| Hybrid electric vehicle energy management with computer vision and deep reinforcement learning Y Wang, H Tan, Y Wu, J Peng IEEE Transactions on Industrial Informatics 17 (6), 3857-3868, 2020 | 41 | 2020 |
| Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm Y Wu, H Tan, L Qin, B Ran Transportation research part C: emerging technologies 117, 102649, 2020 | 39 | 2020 |
| Robust missing traffic flow imputation considering nonnegativity and road capacity H Tan, Y Wu, B Cheng, W Wang, B Ran Mathematical Problems in Engineering 2014, 2014 | 39 | 2014 |
| A fused CP factorization method for incomplete tensors Y Wu, H Tan, Y Li, J Zhang, X Chen IEEE transactions on neural networks and learning systems 30 (3), 751-764, 2018 | 37 | 2018 |
| Robust tensor decomposition based on Cauchy distribution and its applications Y Wu, H Tan, Y Li, F Li, H He Neurocomputing 223, 107-117, 2017 | 35 | 2017 |
| Short-term traffic flow prediction based on multilinear analysis and k-nearest neighbor regression Y Wu, H Tan, J Peter, B Shen, B Ran CICTP 2015, 556-569, 2015 | 28 | 2015 |
| A new traffic prediction method based on dynamic tensor completion H Tan, Y Wu, G Feng, W Wang, B Ran Procedia-Social and Behavioral Sciences 96, 2431-2442, 2013 | 28 | 2013 |
| Intelligent road infrastructure system (iris): systems and methods B Ran, Y Cheng, S Li, Z Zhang, F Ding, H Tan, Y Wu, S Dong, L Ye, X Li, ... US Patent 10,692,365, 2020 | 27 | 2020 |
| Detection of train driver fatigue and distraction based on forehead EEG: a time-series ensemble learning method C Fan, Y Peng, S Peng, H Zhang, Y Wu, S Kwong IEEE Transactions on Intelligent Transportation Systems 23 (8), 13559-13569, 2021 | 25 | 2021 |
| Traffic flow prediction with missing data imputed by tensor completion methods Q Li, H Tan, Y Wu, L Ye, F Ding IEEE Access 8, 63188-63201, 2020 | 15 | 2020 |
Huachun TanBeijing Institute of TechnologyVerified email at bit.edu.cn
