Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Saturation Prediction for Crowdsensing Based Smart Parking System

  • Kim, Mihui (Dept. of Computer Science and Engineering, Computer System Institute, Hankyong National University) ;
  • Yun, Junhyeok (Dept. of Computer Science and Engineering, Computer System Institute, Hankyong National University)
  • Received : 20181000
  • Accepted : 2019.04.09
  • Published : 2019.12.31
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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.

Keywords

Acknowledgement

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2018R1A2B6009620).

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