Tingting Lu

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.

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.

This study presents a multi-objective optimization model to solve aircraft landing problem within a predetermined time window. Minimizing total fuel cost, minimizing total delays, maximizing throughput, and maximizing fairness are considered to construct the multi-objective optimization model. According to the different weights of objectives, the first-come-first-serve and simulated annealing are used to solve the multi-objective optimization problem. The algorithms’ feasibility is analysed and a suggestion about how to get satisfactory solutions for all stakeholders is given through the expert evaluation method. The results show that the simulated annealing can get better solution than the first-come-first-serve when the number of flights exceeds 10.