Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency.

Air Traffic Flow Management delay (ATFM delay) can quantitatively reflect the spatial and temporal distribution of congestion caused by the imbalance of airspace network capacity and demand, and is an important parameter to evaluate the effectiveness of air traffic flow management (ATFM) strategy. If the distribution of ATFM delay can be predicted in advance, the predictability and effectiveness of ATFM strategy can be improved. In this paper, we propose a short-time ATFM delay spatial and temporal distribution prediction method in the absence of historical records of specific ATFM delay causes.The method first clarifies the prediction object by constructing ATFM delay prediction network model,and proposes the ATFM delay prediction index system; Secondly, we use Infromation Value (IV) to filter and weight the features in order to construct a weighted ATFM delay prediction index system; Then, a support vector machine model based on particle swarm optimization(PSO-SVM) is implemented to predict ATFM delay distribution. Finally, some congested airports and key waypoints in China are selected as ATFM delay prediction network nodes for empirical analysis. The experimental results show that the prediction accuracy of the IV-PSO-SVM model reaches 96.4%, which is 13.5% and 9.1% higher than that of SVM model and PSO-SVM model, respectively.

In order to meet the air traffic demand and capacity balance in the pre-tactical management stage. It is necessary to scientifically adjust the flight pre-flight plan according to the change of airport capacity to avoid waste of flight slot resources and reduce flight delays. Firstly, this paper proposes a multi-level full-time zone conflict resolution algorithm design for the problem of demand imbalance in pre-flight planning. Then, within the scope of the underlying design, the time conflict resolution strategy and resource use conflict resolution strategy of the 5-minute time slice within the specified time period are studied respectively. Finally, the underlying strategy of the algorithm for flight advance planning optimization is validated using real airport operational data as an example, and the strategy is compared by calculating a relative deviation index (RDI) for a specific target value. The results show that the different strategies designed in this paper show different range of results in the process of multi-objective optimization, and the optimization effect is more balanced, which provides a reference for the underlying design of a variety of strategies for the realization of pre-flight plan conflict resolution and demand balance.