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An Analytical Model for Crowdsensing On-street Parking Spaces | IEEE Conference Publication | IEEE Xplore

An Analytical Model for Crowdsensing On-street Parking Spaces


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...Show More

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.
Date of Conference: 20-22 December 2019
Date Added to IEEE Xplore: 09 June 2020
ISBN Information:
Conference Location: Tunis, Tunisia

References

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