| Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns Y Liang, Z Zhao, L Sun Transportation Research Part C: Emerging Technologies 143, 103826, 2022 | 57* | 2022 |
| Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach Y Liang, G Huang, Z Zhao Transportation research part C: emerging technologies 140, 103731, 2022 | 37 | 2022 |
| NetTraj: A network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms Y Liang, Z Zhao IEEE Transactions on Intelligent Transportation Systems 23 (9), 14470-14481, 2021 | 26* | 2021 |
| A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards Z Zhao, Y Liang Transportation Research Part C: Emerging Technologies 149, 104079, 2023 | 20* | 2023 |
| Bike sharing demand prediction based on knowledge sharing across modes: A graph-based deep learning approach Y Liang, G Huang, Z Zhao 2022 IEEE 25th International Conference on Intelligent Transportation …, 2022 | 9 | 2022 |
| Spatial perspectives on coworking spaces and related practices in Beijing H Huang, Y Liu, Y Liang, D Vargas, L Zhang Built Environment 46 (1), 40-54, 2020 | 9 | 2020 |
| Exploring large language models for human mobility prediction under public events Y Liang, Y Liu, X Wang, Z Zhao arXiv preprint arXiv:2311.17351, 2023 | 8 | 2023 |
| Understanding market competition between transportation network companies using big data G Huang, Y Liang, Z Zhao Transportation Research Part A: Policy and Practice 178, 103861, 2023 | 6* | 2023 |
| Cross-mode knowledge adaptation for bike sharing demand prediction using domain-adversarial graph neural networks Y Liang, G Huang, Z Zhao IEEE Transactions on Intelligent Transportation Systems, 2023 | 6 | 2023 |
| Deep trip generation with graph neural networks for bike sharing system expansion Y Liang, F Ding, G Huang, Z Zhao Transportation Research Part C: Emerging Technologies 154, 104241, 2023 | 6 | 2023 |
| Modeling taxi cruising time based on multi-source data: a case study in Shanghai Y Liang, Z Zhao, X Zhang Transportation, 1-30, 2022 | 2 | 2022 |
| POI DATA VERSUS LAND USE DATA, WHICH IS MORE EFFECTIVE IN MODELING THEFT CRIMES? J FENG, Y LIANG, QI HAO, KE XU, W QIU | 2 | |
| Time-dependent trip generation for bike sharing planning: A multi-task memory-augmented graph neural network Y Liang, Z Zhao, F Ding, Y Tang, Z He Information Fusion, 102294, 2024 | | 2024 |
| RouteKG: A knowledge graph-based framework for route prediction on road networks Y Tang, W Deng, S Lei, Y Liang, Z Ma, Z Zhao arXiv preprint arXiv:2310.03617, 2023 | | 2023 |
| Time-Aware Trip Generation for Bike Sharing System Planning Y Liang, F Ding, Y Tang, Z Zhao | | 2023 |
| 北京五环内共享办公与传统办公租金的空间特征与影响因素比较研究 梁月冰 北京规划建设 1, 2020 | | 2020 |
Zhan ZhaoThe University of Hong KongVerified email at hku.hk
