| Variational deep embedding: An unsupervised and generative approach to clustering Z Jiang, Y Zheng, H Tan, B Tang, H Zhou arXiv preprint arXiv:1611.05148, 2016 | 752 | 2016 |
| 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 | 741 | 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 | 396 | 2016 |
| A tensor-based method for missing traffic data completion H Tan, G Feng, J Feng, W Wang, YJ Zhang, F Li Transportation Research Part C: Emerging Technologies 28, 15-27, 2013 | 389 | 2013 |
| 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 | 285 | 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 | 229 | 2020 |
| 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 | 223 | 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 | 153 | 2019 |
| 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 | 149 | 2016 |
| 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 | 105 | 2020 |
| A novel curve lane detection based on Improved River Flow and RANSA H Tan, Y Zhou, Y Zhu, D Yao, K Li 17th international ieee conference on intelligent transportation systems …, 2014 | 104 | 2014 |
| 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 | 91 | 2020 |
| Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model L Li, B Du, Y Wang, L Qin, H Tan Knowledge-Based Systems 194, 105592, 2020 | 86 | 2020 |
| Tensor completion via a multi-linear low-n-rank factorization model H Tan, B Cheng, W Wang, YJ Zhang, B Ran Neurocomputing 133, 161-169, 2014 | 86 | 2014 |
| 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 | 84 | 2020 |
| Detecting eye blink states by tracking iris and eyelids H Tan, YJ Zhang Pattern Recognition Letters 27 (6), 667-675, 2006 | 62 | 2006 |
| 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 | 60 | 2016 |
| 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 | 53 | 2018 |
| Traffic speed data imputation method based on tensor completion B Ran, H Tan, J Feng, Y Liu, W Wang Computational intelligence and neuroscience 2015 (1), 364089, 2015 | 53 | 2015 |
| 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 (1), 763469, 2014 | 53 | 2014 |
