The civil aviation industry is experiencing significant growth in air traffic density within terminal areas, necessitating improved air traffic efficiency. In China’s pursuit of world-class airport clusters, operational complexities arise due to the co-location of these airports in the same terminal area airspace, resulting in lower operational efficiency. To mitigate congestion and flight delays, this study proposes an integrated model that considers multiple runways and route selections, accounting for actual route point restrictions. Utilizing actual operational data from Shanghai metroplex, the proposed model is validated. The study focuses on the airport metroplex system and presents a comprehensive mixed-integer programming (MIP) model for arrival sequencing, considering multiple airports, runways, and routes. The maximum landing efficiency is adopted as the objective function, optimizing arrival scheduling while considering time intervals, route selection, and landing constraints. The Multi-waypoint Rolling Horizon Control (MWRHC) algorithm is employed to tackle time-efficiency challenges, ensuring flight safety by continuous monitoring of flights in the terminal area. Comparative analysis reveals the algorithm’s superior optimization performance for single-runway airports compared to dual-runway airports. Overall, the proposed model and algorithm effectively improve the efficiency of multi-airport arrival scheduling in airport metroplex systems.

In recent years, the rapid development of remote tower technology has made it crucial to accurately assess the situational awareness (SA) levels of remote tower controllers. Such an assessment is significant for controller training and remote tower system design. This study employed the SART scale to compare controllers’ SA scores in traditional and remote tower environments. Results revealed significant differences, especially in attention demand and situational understanding. Subsequently, a quantitative analysis of controllers’ perception, understanding, and decision-making abilities was conducted, integrating subjective and objective data. Eye-tracking, heart rate, working memory scales, and communication-coordination scales showed significant results. Experienced controllers had better psychological safety skills, while trainees were more likely to increase vigilance. Moreover, a series of sensitive SA indicators were identified. An evaluation index system was established using the entropy weight method. By calculating the Euclidean distance, Gray relational degree, and comprehensive proximity coefficient, the SA levels of controllers were comprehensively evaluated. The top five important indicators were average blink rate, scan length, average fixation duration, fixation duration, and average pupil diameter. These findings support enhancing air traffic control safety and refining SA assessment for remote tower controllers.

The assessment of collision risk at intersecting air routes is a crucial method for determining safe separation during aircraft route flights. This paper employs the Monte Carlo method to analyze the operational characteristics of aircraft on intersecting air routes in two stages, integrating the influence of the human-operated adjustment process on the distance between the two aircrafts, so as to propose a collision risk assessment model more aligned with the actual operational process. The initial stage considers the positional errors to establish a distance distribution function between trailing and leading aircraft when the latter reaches the intersection. The subsequent stage calculates the minimum distance between the aircraft by combining the kinematic equations with the controller’s thinking and reactions, communications, the pilot’s reactivity, and time required for aircraft maneuvering. Ultimately, based on the intersection course configuration, Monte Carlo simulations were employed to assess the impact of variables, such as magnetic course and aircraft speed distribution, on collision risk. The study’s findings highlight the significance of aircraft positioning capabilities and speed maintenance performance. It also reveals that the collision risk results, simulated from various perspectives, offer a theoretical foundation for the development of intersecting air routes.

To address the insufficient evaluation of scenario adaptability in the coordinated control of shared waypoints within multi-airport systems, this study proposes two optimization strategies: the Multi-Waypoint Rolling Horizon Control (MWRHC) and the Multi-Waypoint Ant Colony Optimization (MWACO) algorithms. A systematic comparison of their applicability and control performance is conducted. Using empirical data from peak-hour operations in the Yangtze River Delta multi-airport system, the applicability and optimization effectiveness of both algorithms in arrival–departure sequencing are evaluated. The metric “Average Flight Time Improvement” is introduced to quantify and compare the performance of different airports during the optimization process, thereby revealing the operational characteristics of MWRHC and MWACO under varying traffic conditions. The results demonstrate that the MWACO algorithm exhibits superior global optimization capability in high-traffic airport environments, whereas the MWRHC algorithm performs better in local optimization and real-time scheduling under moderate traffic conditions.

Performing an operational safety situation evaluation of the terminal area control system is crucial for enhancing safety management and ensuring operational safety in the terminal area. We use a combination active–passive risk source identification method to thoroughly identify the safety features of the terminal area control system, and then establish a related indicator system. After that, the system’s secondary and primary indicators are used as input and output vectors for an Extreme Learning Machine (ELM). This creates an ISSA-ELM-based risk probability prediction model for the primary indicators, which gives us the predicted values of the primary indicator risks. Bayesian theory merges the output results to determine the overall safety status of the terminal area control system. The evaluation results indicate a distinct correlation between the secondary and primary indicators in a terminal area of North China. The ELM, which has been improved by the Sparrow Search Algorithm (ISSA), correctly displays this mapping and predicts the operational risks. The improved model is able to make predictions that are more than 80% accurate. A comparison of the model’s evaluation results with the actual operational conditions during the same period demonstrates consistency, suggesting that this method may consistently evaluate the operational safety status of the terminal area control system.

Air traffic flow converges from the route to the terminal area and then disperses to the airport. Research of the transmit mechanism has a significant importance on the efficiency control of queue length and congestion of the terminal area. In this process, the corresponding convergence wave and dissipation wave will come into being. In this paper, the basic principle of the traffic-wave is introduced. A traffic-wave equation suitable for air traffic flow is established, based on the Underwood speed-density model. In view of the particularity of air traffic flow, we will analyze the density of air traffic flow to research the propagation speed of the convergence wave and dissipation wave, and then we will obtain the mechanism of the queue length of the terminal area. To ensure the stability of the queue length, the relation of transmit time between the convergence wave and dissipation wave is also proposed. Finally, some examples will be used to validate the practicability and feasibility of the algorithms proposed above.