| Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels Y He, Y Liu, L Yang, X Qu IEEE Transactions on Intelligent Vehicles, 2023 | 50 | 2023 |
| Deep demand prediction: An enhanced conformer model with cold-start adaptation for origin–destination ride-hailing demand prediction H Lin, Y He, Y Liu, K Gao, X Qu IEEE Intelligent Transportation Systems Magazine, 2023 | 19 | 2023 |
| Charting the future: Intelligent and connected vehicles reshaping the bus system K Wang, Y Xiao, Y He Journal of Intelligent and Connected Vehicles, 2023 | 15 | 2023 |
| Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models H Lin, C Lyu, Y He, Y Liu, K Gao, X Qu IEEE Transactions on Vehicular Technology, 2024 | 9 | 2024 |
| Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems H Lin, Y He, S Li, Y Liu Journal of Transportation Engineering, Part A: Systems 150 (2), 04023132, 2024 | 8 | 2024 |
| A deep learning method for traffic light status recognition L Yang, Z He, X Zhao, S Fang, J Yuan, Y He, S Li, S Liu Journal of Intelligent and Connected Vehicles 6 (3), 173-182, 2023 | 7 | 2023 |
| Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms Y He, Y Liu, L Yang, X Qu Transportation Letters, 1-15, 2024 | 5 | 2024 |
| Importance calculation of nodes in complex network based on improved TOPSIS Y He, S Lei, X Ren 2019 2nd International Conference on Information Systems and Computer Aided …, 2019 | 2 | 2019 |
| Hongyi Lin Y He, Y Liu, K Gao, X Qu | | 2023 |