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Title	An Efficient Parking Allocation Scheme Based On K-Means Clustering Method
ID_Doc	7878
Authors	Smrithii R.; Nithya B.; Jinila Y.B.; Shyry S.P.; Subhashini R.
Year	2022
Published	4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022 - Proceedings
DOI	http://dx.doi.org/10.1109/ICIRCA54612.2022.9985507
Abstract	With the development of the automobile sector in our country over the years now, the vehicles in use have constantly grown to new heights. As a result, when considering the rate of expansion in the number of motorized vehicles, it greatly outpaces the parking rate facility building. The idea of ongoing amelioration and development of parking distribution systems has always been important to the city's smooth operation. As a result, building an effective and dynamic parking algorithm will be a significant step forward in addressing the problem of urban parking shortages. As a result, utilizing the K-Means Algorithm, this study developed a strategy for parking allocation. The findings of the experiments reveal that a parking prediction and allocation model based on K-Means Clustering can not only anticipate and allocate parking spaces in real time, but it also has the ability to enhance the rate of utilization of different types of parking slots. As a result, it makes a substantial contribution to smarter and more sustainable urban parking management. In this paper an efficient parking allocation model is proposed and the research findings to predict the number of available parking spaces is discussed. © 2022 IEEE.
Author Keywords	Dynamic parking model; K-Means clustering; parking allocation; smart cities

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