Airspace Sector Probabilistic Traffic Demand Prediction Model

In order to predict the traffic demands and variations for a future time interval in an airspace sector, the influence of the aircraft flight time on the probabilistic traffic demand prediction was analyzed from the point of uncertainty with a simplified airspace structure. The probabilistic distribution functions for the sector entry time, sector exit time and sector transit time were established by analysis of the randomicity of the sector entry, exit and transit process. On this basis, an airspace sector probabilistic traffic demand prediction model was developed, and a heuristic algorithm was designed. A simulation was performed to verify the proposed model and algorithm using real flight data. The result shows that the sector congestion predicted by the model and algorithm occurs during 10:00-11:00. Compared with the traditional deterministic methods, the proposed method can reduce 30 min of congestion period avoid unnecessary flow management during the period 9:30-10:00, and thus alleviate the air traffic controllers' workload.