Because of more and more flight delays due to the increasing air traffic demands, merely depending on a mono-objective programming to solve problems such as departure slot, flight route and air traffic control (ATC) workload cannot meet the need of the air traffic flow management. In this paper, a multi-objective and non-linear model is developed, comprehensively considering the problems above and picking up factors causing airspace congestion and flight delays. Then, a multi-objective genetic algorithm is designed to solve this model. The simulation results based on the operational flight data prove that the model and the algorithm cannot only optimize the departure time and route for each flight within reasonable time-horizon, but also reduce the ATC workload. It makes air traffic flow management coincident with the actual operation, and finally effectively reduces the airspace congestion.

To be different from the traditional concept of congestion, congestion propagation based on the correlation between aircraft is given. And the main resource shared and competed for in airspace is the air route network, especially the intersection linking the multiroute. The system composed of congestion propagation units operates in airspace network, which is limited by the network geometry and the correlation between aircraft. This paper presents models based on the congestion and propagation characteristics in complex network, predicting the trend of congestion propagation and the peak of congestion size. By analyzing the relationships between system parameters and congestion propagation and accounting for the effects of propagation across networks, this paper enhances the current dynamics models of congestion propagation in airspace. Firstly, a heterogeneous network model is introduced to reveal the propagation process of aircraft with different degrees of correlation. This is followed by the specification of two simplified models for short-term prediction, just taking the sector capacity, propagation rate, and dissipation rate into account. And the propagation rate and dissipation rate depend on the sector geometry and aircraft distribution. Using them (sector capacity, propagation rate, and dissipation rate), the prediction models are accurate in predicting the evolution of congestion peak and propagation trend in comparison with the sample data of intersections in the sector. Of them, the model with capacity limitation is more accurate on busy hour. And on non-busy hour, capacity is insensitive in predicting congestion clusters. Furthermore, the computing method of propagation rate and dissipation rate is given in our paper. Finally, a numerical analysis is performed, in which it is demonstrated that system capacity, propagation rate, and dissipation rate have different effects on congestion propagation in airspace. The results show that low propagation and high dissipation rates not only are nonlinear but also decrease the level of congestion in the propagation of congestion. In particular, of the three parameters, system capacity affects the rate of convergence, with a low-capacity system reaching a stable state quickly and therefore providing a basis for sector partitioning. The method proposed in this paper should enable air traffic controllers to better understand the characteristics of congestion and its propagation for the benefits of both congestion management and improvement of efficiency. Significantly, airspace designers can take congestion propagation into consideration for optimizing the airspace structure in the future.

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.

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.

Deconfliction of the 4D trajectory in the strategic phase has important significance to the realization of the 4D trajectory based concept of the operations in the next generation air transportation system. Although there is uncertainty, to solve the strategic trajectory deconfliction problem, this paper constructed a deconfliction model which combined the departure time adjustment and the flight level allocation under consideration of the uncertainty of aircraft location and arrival time at the waypoints. The objective function comprehensively considered three types of deconfliction costs including the probabilistic conflict resolution cost, the departure time, and the flight level adjustment cost. The simulation environment was conducted by choosing a 4D trajectory in the central south region of China. Stochastic optimization models for the strategic trajectory deconfliction were constructed and are solved using the Tabucol algorithm. Analysis was made of the impact of different model parameters and the prediction uncertainty of 4D trajectory on the trajectory conflict deconfliction results. The results indicate that the model can realize the comprehensive optimization for minimizing both the robustness of the trajectories operations and the adjustment cost of the departure time and flight level.