To fully leverage the spatiotemporal dynamic correlations in air traffic flow and enhance the accuracy of traffic flow prediction models, thereby providing a more precise basis for perceiving congestion situations in the air route network, a study was conducted on a traffic flow prediction method based on deep learning considering spatiotemporal factors. A waypoint network topology graph was constructed, and a neural network model called graph convolution and self-attention-based long short-term memory neural network (GC-SALSTM) was proposed. This model utilized waypoint flow and network efficiency loss rate as input features, with graph convolution extracting spatial features from the waypoint network. Additionally, a long short-term memory network based on a self-attention mechanism was used to extract temporal features, achieving accurate prediction of waypoint traffic. An example analysis was performed on a typical busy sector of airports in the Central and Southern China region. The effectiveness of adding the network efficiency loss rate as an input feature to improve the accuracy of critical waypoint traffic prediction was validated. The performance of the proposed model was compared with various typical prediction models. The results indicated that, with the addition of the network efficiency loss rate, the root mean square error (RMSE) for eight waypoints decreased by more than 10%. Compared to the historical average (HA), autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), and graph convolution network and long short-term memory network (GCN-LSTM) models, the RMSE of the proposed model decreased by 11.78%, 5.55%, 0.29%, 2.53%, and 1.09%, respectively. This suggests that the adopted network efficiency loss rate indicator effectively enhances prediction accuracy, and the constructed model exhibits superior predictive performance in short-term waypoint traffic forecasting compared to other prediction models. It contributes to optimizing flight paths and high-altitude air routes, minimizing flight delays and airborne congestion to the greatest extent, thus enhancing the overall efficiency of the entire aviation system.

Rational decision making in transport related investment programs greatly depends on the valuation of travel time savings. The primary objective of this study is to estimate the social value of travel time savings (SVTTS) in the city of Nanjing, China. A stated preference (SP) survey was conducted to investigate travelers’ willingness to pay (WTP) for travel time savings. Face-to-face interviews were conducted at stations, shopping centers, schools, and parks in different urban districts in Nanjing. The binary and mixed logit models were developed. The results revealed that the random coefficient binary logit model is the superior model. The factors that significantly affect respondents’ WTP for travel time savings include age, income, purpose, trip mode, distance of the recent trip, cost of the recent trip, whether people conduct other activities during their trip, reduction in travel time, and change in travel cost. The Monte Carlo simulation method was used to generate the distribution of the subject value of travel time savings (VTTS). Based on the mixed logit model, VTTS has a mean value of 35.80 RMB/h with a standard deviation of 6.10 RMB/h. The tax system is used to illustrate the contribution of different income groups to social funds, and the social value of statistical life was also calculated. The estimated SVTTS was found to be 43.72 RMB/h.

To further enhance fairness in the allocation of en-route space–time resources in the collaborative trajectory selection program, a study on the plan preferences between air traffic control (ATC) and airlines in the selection process of the final plan is conducted based on the initial resource allocation plans, considering the roles of airlines in resources allocation decisions. By using Stackelberg game theory, a game model is established for the roles played by ATC and airlines in the process of selecting plans. Then, combining the overall consideration of ATC for all affected flights, the preferences of airlines for initial allocation plans are obtained, and the option range of selectable plans is narrowed down to determine the optimal allocation plan. The results of the example analysis show that the proposed model and method can effectively select the optimal allocation plan from the six initial allocation plans, select the trajectories and entry slots in the congestion areas for airlines that better meet the operation demand, and provide the decision basis with more preferences for ATC to select the final allocation plan. When ATC prefers the lowest overall delay cost, the delay cost of the selected optimal allocation plan is 267.7 min, which is 23.84% lower than the traditional RBS algorithm; when considering the preferences of the main base airline in East China, the delay cost of the selected optimal allocation plan is 287.7 min, which is 18.15% lower than the traditional RBS algorithm.

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.