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Smart city article details

Title	Listen-On-Edge: Towards Learning Realtime Traffic Flow In Smart City
ID_Doc	35355
Authors	Hu L.; Yang Y.; Sun G.; Duan R.; Ding X.
Year	2021
Published	Proceedings - 2021 7th International Conference on Big Data Computing and Communications, BigCom 2021
DOI	http://dx.doi.org/10.1109/BigCom53800.2021.00032
Abstract	As urbanization moves into making smart cities, intelligent transportation applications have recently sprung up, creating a bad need of realtime traffic flow information. However, legacy approaches to estimating or predicting traffic flow fall short in pursuing urban-scale realtime traffic flow. In this paper, we turn to a new idea: deploying lightweight deep learning on smartphone-like mobile edge devices to achieve realtime traffic flow estimation in urban scale. Specifically, we design a cloud-free system, Deeput, which resides in mobile edge devices and listens to vehicular traffic noise along roads. Locally at these devices, Deeput maps the recorded traffic noise into spectrograms, and then, infers realtime traffic flow from these spectrograms by a novel deep learning model. We build a prototype of Deeput that can smoothly run on Android smartphone, and the experimental results show Deeput's efficacy and efficiency. The Deeput designs are also insightful for realizing urban-scale, realtime applications that require or target on-edge intelligence. © 2021 IEEE.
Author Keywords	deep learning; realtime traffic flow; smart city

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