In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management.

With the development of air transport industry in China, the congestion problem in the terminal areas of busy airports has become increasingly serious. In order to alleviate the increasingly frequent air traffic congestion, it is necessary to accurately and objectively predict traffic flow. Traditionally, most predicted methods are based on the number of aircrafts flight in the terminal area to obtain deterministic traffic flow data, without considering the impact of uncertain factors on the prediction results. Based on the uncertainty of demand, this paper uses a probability density prediction method based on quantile regression neural network and kernel density estimation, to analyse the variation of traffic flow at different quantiles according to the obtained continuous conditional quantile function. Predicting the probability density of traffic flow on a certain day, and then comparing the point prediction value corresponding to the peak value, which consider the weather factor and the conditional probability density prediction curve without considering the weather factor, it is concluded that considering the weather factor can make the traffic flow prediction more accurate.

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In order to solve the problem of cooperative allocation of route resources in the case of congestion or bad weather, this paper proposes a two-stage time slot coordinated allocation model. The two phases will be considered separately from the air traffic control department and the airlines, with the lowest total delay cost of the affected flights and the average flight delay cost between the airlines tend to be the same. In order to verify the model, this paper intends to use the greedy algorithm to solve. The validity of the model is tested by the simulation data of the route, so as to achieve the goal of balancing multiple flow constrained areas and improve the operation efficiency of the route.