A data-driven approach for determining airport declared capacity

Abstract

Slot-control policies have been widely implemented in congested airports worldwide to manage air transportation demand and airport capacity. These airports, known as slot-coordinated airports or Level 3 airports, provide a certain number of airport slots to airlines. The number of slots in a given time period — for example, 15 min or 1 h — is generally referred to as declared capacity. Only those airlines that obtain airport slots can schedule regular flights at the given airport for the next summer or winter season. Much effort has been made to optimize allocation schemes for the slot coordinator, such that airport slots are distributed to airlines efficiently and effectively. However, few attempts have been made to determine optimal airport declared capacity, mainly because of two challenges. First, the stochastic nature of airport operations due to uncertainty. Second, the unclear relationship between the economic benefits gained by scheduled flights and the possible cost induced by congestion or lost revenue due to unused airport capacity. This paper presents a novel approach for determining airport declared capacity. First, we present a K-medoids-DTW method to cluster airport capacity scenarios and a step-queuing pricing mechanism based on the zero-queuing pricing model to quantify the cost of operations in different time periods of the day. A two-stage stochastic programming model is then developed to determine the number of slots at the airport. A busy international Level 3 airport in Asia is used as a case study to demonstrate the applicability of this model. Our results highlight the advantages of the step-queuing pricing model (SPM) compared with the uniform pricing model (UPM). The SPM allows more slots to be created. Our method can be a useful tool for airport authorities or other related aviation authorities seeking to assign the optimal number of slots at different levels of service.