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

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. • 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.