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Title	An Analytical Model For Crowdsensing On-Street Parking Spaces
ID_Doc	7530
Authors	Zheng W.; Liao R.; Zeng J.
Year	2019
Published	2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019 - Proceedings
DOI	http://dx.doi.org/10.1109/IINTEC48298.2019.9112127
Abstract	Parking becomes an insurmountable pain in cities as the world continues to urbanize and the car ownership becomes common. Industry and academia have made great efforts to facilitate parking and lessen the drivers' searching time by utilizing real-time sensing techniques or big data based occupancy prediction algorithms. In this paper, we focus on an innovative crowdsensing way to provide the currently unavailable on-street parking information for smart cities, and analyze the number of sensing units required by the crowdsensing approach. An analytical model is developed to find the relationship among the required sensing units, detection accuracy and update time based on historical parking data derived from Open Data portals of two metropolises in China. The model proves that the crowdsensing approach has a great potential in bringing the on-street parking information to drivers by employing significantly fewer sensing units compared with the traditional fixed sensing alternative. © 2019 IEEE.
Author Keywords	analytic model; crowdsensing; smart parking

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