Mixed clustering algorithm of airport capacity in stochastic GHP model

In order to make full use of airport capacity resources and eliminate existing human prediction error in stochastic ground holding policy(GHP) model, a mixed clustering algorithm was researched. Daily capacity was divided into several intervals in accordance with 30 min, each interval corresponded to a certain capacity value, and the capacity of one day was a capacity scenario. The capacity scenarios of an airport in half a year were collected, typical capacity scenarios were produced by using self-organizing-maps(SOM) neural network and k-means clustering algorithm, and the probability of each capacity scenario was calculated. Typical capacity scenario tree was constructed and applied in stochastic static and dynamic GHP models. Simulation result shows that compared with no-GHP case, the total delay costs of static and dynamic GHP models reduce by 32.7% and 52.7% respectively. So the mixed algorithm is feasible, and the typical capacity scenario tree is practical.