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Smart city article details

Title	Data-Driven Parking Decisions: Proposal Of Parking Availability Prediction Model
ID_Doc	17465
Authors	Kim K.; Koshizuka N.
Year	2019
Published	HONET-ICT 2019 - IEEE 16th International Conference on Smart Cities: Improving Quality of Life using ICT, IoT and AI
DOI	http://dx.doi.org/10.1109/HONET.2019.8908028
Abstract	Due to the increase of the population and car ownership level, car-related problems which represented by traffic congestion and air pollution are caused. Parking problem is one of the most significant issues of them. Especially, long cruising time for parking spaces causes enormous economic cost. Thanks to recent IoT (Internet of Things) technology, it becomes possible to monitor availability of parking lots and some cities are providing monitored information to drivers. This system makes drivers to find parking spots easier than before. Nevertheless, there is a controversy over quality and quantity of availability information. Because of update interval and network delay, provided information is different from the real-Time status (quality issues). Plus, there are still many unmonitored parking lots of which availability information couldn't be read (quantity issues). In this paper, we propose two prediction models for qualitative and quantitative improvement of parking availability information. The proposed solution is evaluated using one month of occupancy rate data, publicly available from Seattle Department of Transportation. © 2019 IEEE.
Author Keywords	Machine Learning; Smart City; Smart Parking; Transportation

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