| DeepPF: A deep learning based architecture for metro passenger flow prediction Y Liu, Z Liu, R Jia Transportation Research Part C: Emerging Technologies 101, 18-34, 2019 | 340 | 2019 |
| A tailored machine learning approach for urban transport network flow estimation Z Liu, Y Liu, Q Meng, Q Cheng Transportation Research Part C: Emerging Technologies 108, 130-150, 2019 | 87 | 2019 |
| DeepTSP: Deep traffic state prediction model based on large-scale empirical data Y Liu, C Lyu, Y Zhang, Z Liu, W Yu, X Qu Communications in transportation research 1, 100012, 2021 | 84 | 2021 |
| Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform Y Liu, F Wu, C Lyu, S Li, J Ye, X Qu Transportation Research Part E: Logistics and Transportation Review 161, 102694, 2022 | 71 | 2022 |
| How machine learning informs ride-hailing services: A survey Y Liu, R Jia, J Ye, X Qu Communications in Transportation Research 2, 100075, 2022 | 65 | 2022 |
| Exploring a large-scale multi-modal transportation recommendation system Y Liu, C Lyu, Z Liu, J Cao Transportation Research Part C: Emerging Technologies 126, 103070, 2021 | 52 | 2021 |
| Can language models be used for real-world urban-delivery route optimization? Y Liu, F Wu, Z Liu, K Wang, F Wang, X Qu The Innovation 4 (6), 2023 | 51 | 2023 |
| Building personalized transportation model for online taxi-hailing demand prediction Z Liu, Y Liu, C Lyu, J Ye IEEE Transactions on Cybernetics 51 (9), 4602-4610, 2020 | 46 | 2020 |
| A spatio‐temporal ensemble method for large‐scale traffic state prediction Y Liu, Z Liu, HL Vu, C Lyu Computer‐Aided Civil and Infrastructure Engineering 35 (1), 26-44, 2020 | 46 | 2020 |
| Attention-based deep ensemble net for large-scale online taxi-hailing demand prediction Y Liu, Z Liu, C Lyu, J Ye IEEE transactions on intelligent transportation systems 21 (11), 4798-4807, 2019 | 45 | 2019 |
| A two-stage destination prediction framework of shared bicycles based on geographical position recommendation Y Liu, R Jia, X Xie, Z Liu IEEE Intelligent Transportation Systems Magazine 11 (1), 42-47, 2018 | 40 | 2018 |
| Automatic feature engineering for bus passenger flow prediction based on modular convolutional neural network Y Liu, C Lyu, X Liu, Z Liu IEEE Transactions on Intelligent Transportation Systems 22 (4), 2349-2358, 2020 | 38 | 2020 |
| Spatio-temporal ensemble method for car-hailing demand prediction Y Liu, C Lyu, A Khadka, W Zhang, Z Liu IEEE Transactions on Intelligent Transportation Systems 21 (12), 5328-5333, 2019 | 37 | 2019 |
| 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 | 22 | 2023 |
| Traffic4cast at neurips 2020-yet more on the unreasonable effectiveness of gridded geo-spatial processes M Kopp, D Kreil, M Neun, D Jonietz, H Martin, P Herruzo, A Gruca, ... NeurIPS 2020 Competition and Demonstration Track, 325-343, 2021 | 22 | 2021 |
| Building effective short video recommendation Y Liu, C Lyu, Z Liu, D Tao 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW …, 2019 | 21 | 2019 |
| Envisioning the future of transportation: Inspiration of ChatGPT and large models X Qu, H Lin, Y Liu Communications in Transportation Research 3, 2023 | 20 | 2023 |
| Behavior2vector: Embedding users’ personalized travel behavior to Vector Y Liu, F Wu, C Lyu, X Liu, Z Liu IEEE Transactions on Intelligent Transportation Systems 23 (7), 8346-8355, 2021 | 20 | 2021 |
| How generative adversarial networks promote the development of intelligent transportation systems: A survey H Lin, Y Liu, S Li, X Qu IEEE/CAA Journal of Automatica Sinica, 2023 | 19 | 2023 |
| Modelling the energy consumption of electric vehicles under uncertain and small data conditions Y Liu, Q Zhang, C Lyu, Z Liu Transportation Research Part A: Policy and Practice 154, 313-328, 2021 | 17 | 2021 |
Cheng LyuTechnical University of MunichVerified email at tum.de
