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Data Analytics for Parking Facility Management

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Advances in Intelligent Networking and Collaborative Systems (INCoS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 527))

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.

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Acknowledgements

This work is partially supported by Mitacs, Natural Sciences and Engineering Research Council of Canada (NSERC), University of Manitoba, as well as Winnipeg Airports Authority (WAA). Also thanks S. Marohn, C. McFadyen, R. Olaes-Zimolag, B. Podaima, T. Strome, R. Wei, and B. Zamorano for their domain expertise.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Deyu Deng, Carson K. Leung & Adam G. M. Pazdor

Authors
  1. Deyu Deng
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  2. Carson K. Leung
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  3. Adam G. M. Pazdor
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Corresponding author

Correspondence to Carson K. Leung .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Science and Technology, Kwansei Gakuin University, Sanda, Japan

    Hiroyoshi Miwa

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Deng, D., Leung, C.K., Pazdor, A.G.M. (2022). Data Analytics for Parking Facility Management. In: Barolli, L., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2022. Lecture Notes in Networks and Systems, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-031-14627-5_12

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