Although the application of new wake turbulence categories, the so-called “RECAT (wake turbulence category re-categorization)”, will realize lower aircraft separation minima and directly increase runway throughput, the impacts of increasing arrival traffic on the surrounding airspace and arrival traffic flow as a whole have not yet been discussed. This paper proposes a data-driven simulation approach and evaluates the effectiveness of the lower aircraft separation in the arrival traffic at the target airport. The maximum runway capacity was clarified using statistics on aircraft types, stochastic distributions of inter-aircraft time and runway occupancy time, and the levels of the automation systems that supported air traffic controllers’ separation work. Based on the estimated available runway capacity, simulation models were proposed by analyzing actual radar track and flight plan data during the 6 months between September 2019 and February 2020, under actual operational constraints and weather conditions. The simulation results showed that the application of RECAT would reduce vectoring time in the terminal area by 7% to 10% under the current airspace and runway capacity when following a first-come first-served arrival sequence. In addition, increasing airspace capacity by 10% in the terminal area could dramatically reduce en-route and takeoff delay times while keeping vectoring time the same as under the current operation in the terminal area. These findings clarified that applying RECAT would contribute to mitigating air traffic congestion close to the airport, and to reducing delay times in arrival traffic as a whole while increasing runway throughput. The simulation results demonstrated the relevance of the theoretical results given by queue-based approaches in the authors’ past studies.

There are ongoing research efforts to implement En route arrival manager (AMAN), which decides arrival runways and controls cruise speed in en route airspace. Air traffic control operations that regulate arrival air traffic flows from en route airspace are considered effective in mitigating the congestion close to destination airports. Therefore, this study proposes a scientific system design for operationally feasible En Route AMAN assisting air traffic controllers (ATCos) through runway-flow and inter-aircraft control. Herein, we devise an airline-oriented runway assignment rule that selects a target minimizing arrival taxi time in case of over-demand according to the maximum estimated through the stochastic distribution of inter-aircraft time and runway occupancy time. We also formulate speed control rules based on inter-aircraft spacing using simulation-based optimization and decision tree analysis to visualize the distinct strategies and rules for the traffic responsible for each ATCo. Furthermore, an agent-based simulation is performed to evaluate the system effectiveness in reducing the arrival delay. The simulation indicates 20-d arrival and departure at the Tokyo International Airport, Japan, between 06:00 and 23:00. The results show that the designed IF–THEN rules reduce the total arrival sequencing delay time and arrival taxi time by 21% (median, 55.8 s) and 6.9% (median, 24.6 s). Our findings suggest that truly optimal scheduled time of arrival (STA) and operationally feasible rules for ATCos could promise congestion relief while ensuring the interpretability and possibility of En Route AMAN implementation.

Although schedule design has further potential to reduce airline operation costs and flight delay, the effectiveness of the globally optimal schedule design integrating air traffic flow has not been discussed thus far. This paper presents a global multi-objective takeoff time optimization to design efficient flight schedules that lead to minimal congestion and provide sufficient resilience against traffic problems. NSGA-II is adopted as the multi-objective optimization technique in this study. The objective functions include minimization of the total arrival delay and total fuel consumption because these are key performance indicators of air traffic management (ATM). The design variable used in this study is the takeoff time offset of each flight landing at the Tokyo International Airport. 607 design variables were used in this study. The range of the design variables was ±300 s to investigate the effect of a minor variation in the takeoff time. A cellular automaton-based model was utilized to simulate the interaction of the flights with each other. The results of the simulations demonstrated that the obtained optimal solutions could drastically reduce the total arrival delay and total fuel consumption by 1500 min and 80 tons, respectively. The spacing adjustments of one of the optimum flight schedules, in comparison to the original flight schedule, were reduced by 80% in the en-route and terminal airspaces. Additional analyses suggest that it is preferable to have longer takeoff time intervals for flights originating from the same point during congestion hours than those during non-congestion hours. This indicates that the optimization of ground movements in airports improves the efficiency of air traffic operations.

The advancement of Arrival MANager (AMAN) is crucial for addressing the increasing complexity and demand of modern airspace. This study evaluates the operational feasibility and effectiveness of an innovative AMAN designed for en route airspace, the so-called En Route AMAN. The En Route AMAN functions as a controller support system, facilitating the sharing of information between en route air traffic controllers (ATCos), approach controllers (current AMAN), and airport controllers (Departure Managers) in airports with multiple runways. The En Route AMAN aims to support upstream ATCos by sequencing and spacing of incoming streams via speed control and runway assignment, thereby enhancing overall air traffic efficiency. Human-In-The-Loop simulations involving rated ATCos are performed under scenarios that replicate real-world traffic and weather conditions. These simulations focus on upstream airspace to assess the impact of En Route AMAN on delay mitigation and ATCos’ performance. Unlike previous studies that solely relied on theoretical models and fast-time simulation for operational feasibility evaluation, this approach incorporates ATCos’ real-time decision-making, situational awareness, and task management, addressing critical operationalization challenges. The results demonstrated that the En Route AMAN could reduce the average flight duration by up to 25.6 s and decrease the total number of ATCo instructions by up to 20% during peak traffic volume. These findings support that the En Route AMAN is both operationally viable and effective in mitigating arrival delays, highlighting the importance of Human-In-The-Loop for practical validation.

Air-transport infrastructure is facing increasing congestion, necessitating innovative approaches to optimize arrival management and minimize delays near airports. To address this issue, this study developed an en route arrival management framework that employs a learning classifier system (LCS) to reduce arrival delays and fuel consumption by generating adaptive and interpretable speed-control rules. A virtual design database, created from multiobjective optimization and integrated with a cellular automaton (CA)-based air-traffic model, facilitates the development of these rules. Within this database, a binary target variable represents speed-control decisions, whereas explanatory variables encompass factors such as aircraft proximity and congestion levels. The LCS is trained on this dataset to generate speed-control rules, and their effectiveness is evaluated through CA-based traffic simulations. The results indicate that the LCS achieved 96.8% accuracy in predicting appropriate speed-control actions, significantly outperforming traditional decision-tree methods (70.5% accuracy). Furthermore, experimental findings demonstrate reductions in average flight times by 10–20 s and up to 5 fewer spacing adjustments for traffic within 600 s before and after the arrival of speed-controlled flights. Overall, the LCS reduced total flight time by 1,801 s and total fuel consumption by 1,650.1 kg per operational cycle, resulting in an estimated annual cost savings of $466,000. Furthermore, the LCS approach successfully extracts both sector-specific and common rules, offering enhanced adaptability to various traffic scenarios. These results suggest that the ability to generalize rules across different airspace sectors can improve the safety and efficiency of air-traffic management.

The performance of Multi-Objective Evolutionary Algorithms (MOEAs) depends on the various parameter settings such as population size, generation size, crossover, mutation and so on. It is often difficult to know the appropriate parameter setting for a real-world optimization problem in advance. Besides, the optimal parameter values might depend on each optimization problem and MOEA itself. However, there are few studies for investigating the effect of parameters even in benchmark problems. Therefore, in this study, the effects on performance due to the crossover operators and MOEAs are widely investigated by using eight benchmark problems, including DTLZ and WFG benchmark problems. The number of objectives is set to three and six. We consider five major crossover operators: Simulated Binary crossover (SBX), Simplex crossover (SPX), Differential Evolution operator (DE), Parent Centric crossover (PCX), and Unimodal Normal Distribution crossover (UNDX). As MOEAs, we adopt Non-dominated sorting genetic algorithm-II (NSGAII), Non-dominated sorting genetic algorithm-III (NSGA-III),-Dominance-based Evolutionary Algorithm (-MOEA), Indicator-Based Evolutionary Algorithm (IBEA) and Multi-Objective Evolutionary Algorithm with decomposition (MOEA/D) in this study. The experimental results on benchmark problems show that the effect of the crossover operator on each MOEA is almost the same in both three and six objectives. This indicates that the knowledge has been obtained so far could adapt to the other MOEAs and more than three objectives. In addition, parameters of some crossover operators such as SBX have little impact on the performance. This indicates that these crossover operators can be set to a value used so far without the need of tuning.