Name
Abstract | ||
|---|---|---|
Monitoring urban flow timely and accurately is crucial for many industrial applications - from urban planning to traffic control in the smart cities. This work introduces a new method for inferring fine-grained urban flow with the internet of mobile things such as taxis and bikes. We tackle the problem from a new perspective and present a novel deep learning method UrbanODE (Urban flow inference with Neural Ordinary Differential Equations). Furthermore, UrbanODE provides a flexible balance between flow inference accuracy and computational efficiency, which is important in computation restricted scenarios such as pervasive edge computing. Extensive evaluations on real-world traffic flow data demonstrate the superiority of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414134 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Urban flow, super-resolution, ordinary differential equations, internet of mobile things, attention mechanism | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhou | 1 | 101 | 23.20 |
| Xin Jing | 2 | 0 | 0.68 |
| Liang Li | 3 | 30 | 6.61 |
| Zhong Ting | 4 | 46 | 11.07 |