Abstract Various solutions have been proposed to alleviate the shortage of parking places, including parking reservation systems and shared-parking systems. In such systems, drivers submit their parking requests in advance, especially their arrival and departure time. Then, the systems will reserve a proper parking spot for a driver if his/her parking request is accepted. However, the driver may arrive earlier or depart later, which may cause service failure. In shared-parking systems, the distributions of commuters’ arrival/departure time have fixed patterns and may be learned based on historical data. Given the distributions of drivers’ arrival/departure time, this paper proposes a Chance-constraint optimization model to solve the reservation and allocation problem for the shared-parking platform. This model aims to maximize the parking utilization level (i.e., the expectation of total occupied parking hours) and keep the service failure rate below a threshold value. We propose a rule-based mixed-integer linear programming to seek a satisfying solution to this model. Numerical tests show that our model performs better than baseline models in indicators such as parking utilization level and service failure rate.

    Highlights We propose a reservation and allocation model which considers the uncertainty in drivers’ arrival/departure time. We aim to maximize the utilization level under the constraint that the service failure rate should be below a preselected threshold value. A chance-constrained optimization model is formulated. We propose a rule-based mixed-integer linear programming to solve it.


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    Title :

    A reservation and allocation model for shared-parking addressing the uncertainty in drivers’ arrival/departure time


    Contributors:
    Wang, Shuofeng (author) / Li, Zhiheng (author) / Xie, Na (author)


    Publication date :

    2021-11-15




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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