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Title	Inferring High-Resolutional Urban Flow With Internet Of Mobile Things
ID_Doc	31316
Authors	Zhou F.; Jing X.; Li L.; Zhong T.
Year	2021
Published	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2021-June
DOI	http://dx.doi.org/10.1109/ICASSP39728.2021.9414134
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. ©2021 IEEE
Author Keywords	Attention mechanism; Internet of mobile things; Ordinary differential equations; Super-resolution; Urban flow

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