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Title	Data Analytics For Parking Facility Management
ID_Doc	17150
Authors	Deng D.; Leung C.K.; Pazdor A.G.M.
Year	2022
Published	Lecture Notes in Networks and Systems, 527 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-14627-5_12
Abstract	Although urbanization benefits modern society and residents of urban cities, limited public resources—such as parking facilities—remain a problem. Parking pricing acts as a tool to adjust the available resources. How should parking pricing be used to maximize parking resource utilization while optimizing the parking revenue for parking management? In this paper, we present a system that utilizes available public resources while optimizing revenue with predefined restrictions, especially in the parking management field. More specifically, we design a data-driven time-series based prediction system, which can support dynamic pricing. Evaluation results show the effectiveness and practicality of our parking data analytics system for supporting parking facility management and dynamic pricing for parking applications. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Big data; Constraint; Data analytics; Dynamic pricing; Intelligent system; Parking; Smart city

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