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Function Replacement Decision-Making for Parking Space Renewal Based on Association Rules Mining

Author

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  • Bing Xia

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    Center for Balance Architecture, Zhejiang University, Hangzhou 310028, China)

  • Yichen Ruan

    (School of Spatial Planning and Design, Zhejiang University City College, Hangzhou 310015, China)

Abstract

Parking lots are typical urban spaces with a large total area and scattered distribution. With the development of smart cars and shared driving, parking demand is likely to decline. Thus, the reuse of existing parking spaces presents important opportunities and challenges in the process of the digital transformation of future cities. One of the key issues in the sustainable renewal of parking spaces is to make scientific decisions regarding the replacement of functions. Based on relevant data from the urban area of Hangzhou, this study analyzes the spatial co-location relationships between parking spaces and other urban points of interest (POIs). By mining the function association patterns, this research aims to establish a decision-making support model for the function replacement of parking spaces. The following conclusions are drawn: (1) based on charge, size, and affiliation, parking lots can be divided into eight categories; (2) parking lots of different charges, sizes, and affiliations differ in their spatial co-location relationships with POIs; and (3) most parking lots are suitable for catering services, followed by companies and commercial residences. The innovations of this research lie in providing scientific references for the renewal of urban fragmented spaces by mining urban function association rules at the microscale.

Suggested Citation

  • Bing Xia & Yichen Ruan, 2022. "Function Replacement Decision-Making for Parking Space Renewal Based on Association Rules Mining," Land, MDPI, vol. 11(2), pages 1-23, January.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:2:p:156-:d:728612
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    References listed on IDEAS

    as
    1. Bing Xia & Jindong Wu & Jiaqi Wang & Yitao Fang & Haodi Shen & Jingli Shen, 2021. "Sustainable Renewal Methods of Urban Public Parking Spaces under the Scenario of Shared Autonomous Vehicles (SAV): A Review and a Proposal," Sustainability, MDPI, vol. 13(7), pages 1-21, March.
    2. Dahlen Silva & Dávid Földes & Csaba Csiszár, 2021. "Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analysing Method," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    3. Fábio Duarte & Carlo Ratti, 2018. "The Impact of Autonomous Vehicles on Cities: A Review," Journal of Urban Technology, Taylor & Francis Journals, vol. 25(4), pages 3-18, October.
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    Cited by:

    1. Tao Wang & Sixuan Li & Wenyong Li & Quan Yuan & Jun Chen & Xiang Tang, 2023. "A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information," Sustainability, MDPI, vol. 15(9), pages 1-25, April.

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