Stochastic Optimisation Models for Air Traffic Flow Management
Air traffic delay is not only a source of inconvenience to the aviation passenger, but also a major deterrent to the optimisation of airport utility. Many developing countries do less to abate this otherwise seemingly invisible constraint to development. The overall objective of this study was to investigate the dynamics of air traffic delays and to develop stochastic optimisation models that mitigate delays and facilitate efficient air traffic flow management. Aviation and meteorological data sources at Entebbe International Airport for the period 2004 to 2008 on daily basis were used for exploratory data analysis, modelling and simulation purposes. Exploratory data analysis involved logistic modeling for which post-logistic model analysis estimated the average probability of departure delay to be 49 percent while that for arrival delay was 36 percent. These computations were based on a delay threshold level at 60 percent which presented more significant predicators of nine and ten for departure and arrival respectively. The proportion of aircrafts that delay was established to follow an autoregressive integrated moving average, ARIMA (1,1,1) time series.
URIRonald, W. (2010). Stochastic optimisation models for air traffic flow management (Doctoral dissertation, Makerere University).