| Paper ID | ASPS-4.2 |
| Paper Title |
INFERRING HIGH-RESOLUTIONAL URBAN FLOW WITH INTERNET OF MOBILE THINGS |
| Authors |
Fan Zhou, Xin Jing, Liang Li, Ting Zhong, University of Electronic Science and Technology of China, China |
| Session | ASPS-4: Autonomous Systems |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Signal Processing over IoT [OTH-IoT] |
| IEEE Xplore Open Preview |
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| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
