Saturation Prediction for Crowdsensing Based Smart Parking System

Mihui Kim and Junhyeok Yun
Online First Paper
10.3745/JIPS.03.0123
Keywords: Crowdsensing, Regression Model, Saturation Prediction, Smart Parking System
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Abstract
Crowdsensing technologies can improve the efficiency of smart parking system in comparison with present sensor based smart parking system because of low install price and no restriction caused by sensor installation. A lot of sensing data is necessary to predict parking lot saturation in real-time. However in real world, it is hard to reach the required number of sensing data. In this paper, we model a saturation predication combining a time-based prediction model and a sensing data-based prediction model. The time-based model predicts saturation in aspects of parking lot location and time. The sensing data-based model predicts the degree of saturation of the parking lot with high accuracy based on the degree of saturation predicted from the first model, the saturation information in the sensing data, and the number of parking spaces in the sensing data. We perform prediction model learning with real sensing data gathered from a specific parking lot. We also evaluate the performance of the predictive model and show its efficiency and feasibility.

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Cite this article
IEEE Style
M. K. J. Yun, "Saturation Prediction for Crowdsensing Based Smart Parking System," Journal of Information Processing Systems. DOI: 10.3745/JIPS.03.0123.

ACM Style
Mihui Kim and Junhyeok Yun, "Saturation Prediction for Crowdsensing Based Smart Parking System," Journal of Information Processing Systems, DOI: 10.3745/JIPS.03.0123.