Name
Abstract | ||
|---|---|---|
Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This task is challenging due to the complex spatial and temporal correlations (e.g., the constraints of road network and the law of dynamic change with time). Existing work tried to solve this problem by exploiting a variety of spatiotemporal models. However, we observe that more semantic pair-wise correlat... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TITS.2020.2983763 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Correlation,Roads,Semantics,Predictive models,Machine learning,Convolution,Analytical models | Journal | 22 |
Issue | ISSN | Citations |
6 | 1524-9050 | 5 |
PageRank | References | Authors |
0.43 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingqi Lv | 1 | 18 | 3.81 |
| Zhaoxiong Hong | 2 | 5 | 0.43 |
| Ling Chen | 3 | 1138 | 114.76 |
| Tieming Chen | 4 | 29 | 5.11 |
| Tiantian Zhu | 5 | 9 | 3.91 |
| Shouling Ji | 6 | 83 | 20.52 |