Name
Title | ||
|---|---|---|
Spatiotemporal Graph Neural Network for Traffic Prediction Exploiting Cascading Behavior |
Abstract | ||
|---|---|---|
As a critical part of the intelligent transportation system, traffic prediction is challenging due to the time-evolving cascading behavior, i.e., the fluctuation of traffic conditions on one road will affect neighboring roads in the future. To address this issue, we propose a novel learning framework, which is able to extract the most relevant historical information for prediction by capturing the underlying cascading behavior. An encoder-decoder architecture is adopted, where the historical contextual information of each road is encoded into a sequence of historical embeddings. A spatiotemporal attention mechanism is devised to model the cascading behavior in the embedding space so that the most relevant information for prediction is concentrated. Extensive experiments on a real-world large-scale highway dataset verify the effectiveness of our proposed approach, observing 3% similar to 5% improvement over state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/GLOBECOM46510.2021.9685187 | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
DocType | ISSN | Citations |
Conference | 2334-0983 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoxiang Zhang | 1 | 0 | 0.34 |
| Xiaoying Gan | 2 | 344 | 48.16 |
| Luoyi Fu | 3 | 415 | 58.53 |
| Liyao Xiang | 4 | 21 | 6.50 |
| Haiming Jin | 5 | 104 | 12.12 |