| Title | Multitask Hypergraph Convolutional Networks: A Heterogeneous Traffic Prediction Framework |
| Authors | Wang, Jingcheng Zhang, Yong Wang, Lixun Hu, Yongli Piao, Xinglin Yin, Baocai |
| Affiliation | Beijing Univ Technol, Fac Informat Technol, Beijing Inst Artificial Intelligence, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China Beijing Transportat Operat Coordinat Ctr, Beijing 100161, Peoples R China Peking Univ, Shenzhen Grad Sch, Pengcheng Lab, Shenzhen 518055, Peoples R China Beijing Univ Technol, Beijing Inst Artificial Intelligence, Fac Informat Technol, Beijing 100124, Peoples R China |
| Keywords | FLOW PREDICTION SPEED PREDICTION REGRESSION MODEL |
| Issue Date | May-2022 |
| Publisher | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS |
| Abstract | Traffic prediction methods on a single-source data have achieved excellent results in recent years, especially the Graph Convolutional Networks (GCN) based models with spatio-temporal dependency. In reality, various modes of urban transportation operate simultaneously. They influence and complement each other in common space-time occasions, constituting the transportation system dynamically. Thus, traffic data from multiple sources is ostensibly heterogeneous, but internally correlated. The typical single data driven models are, however, not universally applicable for heterogeneous traffic data. To address this issue, we propose a Multi-task Hypergraph Convolutional Neural Network (MT-HGCN) for the multi-source traffic prediction problem. The framework consists of a main task and a related task. Both tasks are based on Hypergraph Convolutional Neural Networks (HGCN) and are devoted to two prediction problems. Furthermore, the tasks are bridged by a feature compress unit, which models the correlation and shares the latent feature to improve the performance of the main task. The node-level forecasting has been evaluated on historical datasets of Beijing to verify the effectiveness of the proposed method. Compared with the state-of-the-arts, the superior performance of the proposed method can be obtained. |
| URI | http://hdl.handle.net/20.500.11897/643138 |
| ISSN | 1524-9050 |
| DOI | 10.1109/TITS.2022.3168879 |
| Indexed | SCI(E) |
| Appears in Collections: | 深圳研究生院待认领 |
