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Dynamic shared parking for private vehicles in central business districts

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Abstract

With the prosperity of the private vehicle industry, people can envision that parking spaces will be much scarcer than ever, especially in those central business districts (CBDs). As one of the by-products of the sharing economy, shared parking allows people to rent the idle private parking spaces nearby, balancing the parking demand in different districts. However, renters need to comprehensively measure the cost and the distance between the parking space and the destination, sometimes, in which the additional constraints (e.g. time limitation) make the decision more complicated. To solve the aforementioned challenge, a Genetic Algorithm Based Dynamic Shared Parking method is proposed. Firstly, the framework of parking space sharing for private cars in CBDs is constructed. Then, the approach based on NSGA-III is designed to generate the approximately optimal solution according to current requirements and the situation of idle parking spaces. The experiments investigate the impact of different metrics to the optimization objective in detail and the results of follow-up comparisons indicate that our method effectively saves the cost in shared parking and improves the social benefits.

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The data set used in the next section is not publicly available due to them containing information that could compromise research participant consent.

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Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities of Southwest Minzu University (2019NQN05) and 2021 College Student Innovation and Entrepreneurship Training Program of Southwest Minzu University (202110656021).

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Authors and Affiliations

  1. Key Laboratory of Electronic Information of State Ethnic Affairs Commission, College of Electrical Engineering, Southwest Minzu University, Chengdu, 610041, China

    Weijia Hong & Zhiyu You

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  1. Weijia Hong
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  2. Zhiyu You
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Correspondence to Zhiyu You.

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Hong, W., You, Z. Dynamic shared parking for private vehicles in central business districts. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-02923-z

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