Short-Term Traffic Flow Prediction of Airspace Sectors Based on Bayesian Estimation Theory

To accurately forecast the air traffic flow distribution in airspace sectors and its development trend in the future, a short-term traffic flow prediction method based on Bayesian estimation theory is proposed. First, the operational history data of various sectors in the airspace system are extracted by parsing raw radar data of the aircraft within the airspace system. On this basis, an aggregate multi-sector traffic flow model is established. Then, Bayesian estimation theory is adopted to predict the future trend of airspace sector traffic flow and its uncertainty intervals by estimating and updating the optimal parameter of the aggregate multi-sector traffic flow model dynamically. Finally, the proposed method is verified on a set of operational history data of five air route sectors, taking 5 min as one time step to predict the short-term air traffic flow in the next one hour. The results show that the absolute error of the predicted results of more than 85% time steps is less than 3, the average absolute error is less than 2, and the stability of the predicted results is well. The proposed method can adequately reflect the dynamics and uncertainty in the airspace system operation, and hence is well in line with the practice.