Highlights New and realistic operation model for shared parking based on multi-unit exchange. Novel application of trading network mechanism to market design for shared parking. A rebalance procedure for exchange with uncertain cancelations over time. Computational experiments with a real-world case and interesting managerial insights.

    Abstract Shared parking is recognized as a promising solution to tackle the parking space shortage problem in population-dense cities. However, peer-to-peer shared parking by individuals is not easy to implement in such cities, because it requires the consent of the majority of residents and the facilitation of the property management agency. In this paper we propose a new model for shared parking driven by morning commute. We consider the agencies of residential parking lots as the players to make collective decisions on behalf of the property owners, and study the market design problem for this new shared parking model. Specifically, we investigate how to assign to each agency a quota of parking spaces from every other agency that its commuters can book in advance through an e-platform and how to specify transfer prices for exchanges, with the objective to maximize agencies’ welfare. Our problem is a new discrete many-to-many exchange economy with multi-unit valuation. We design a stable exchange scheme based on the trading network theory, and show that this new scheme is efficient and in the core. In the real environment with random cancelations over time, we further develop a rolling-horizon rebalance procedure that minimizes the welfare loss. Computational experiments using real-world data demonstrate that our stable exchange scheme substantially outperforms the commonly adopted solutions based on the first-come-first-serve protocol and also reveal a variety of interesting insights for practice.


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    Title :

    Market design for commute-driven private parking lot sharing


    Contributors:
    Lai, Minghui (author) / Cai, Xiaoqiang (author) / Hu, Qian (author)


    Publication date :

    2020-11-30




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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