Airspace Capacity Research Articles

Attention-LSTM based prediction model for aircraft 4-D trajectory

Aviation activities are constantly increasing as a result of the growth of the global economic system. How to increase airspace capacity within the limited airspace resources while ensuring smooth and safe aircraft operations is a challenge for civil aviation today. Air traffic safety is supported by accurate trajectory prediction. The way-points are relatively sparse, and there are many uncertain factors in the flight, which greatly increases the difficulty of trajectory prediction. So, it is vital to enhance trajectory prediction accuracy. An attention-LSTM trajectory prediction model is proposed in this paper, which is split into two parts. The time-series features of the flight trajectory are extracted in the initial stage using the long-short-term memory neural network (LSTM). In the second part, the attention mechanism is employed to process the extracted sequence features. The impact of secondary elements is reduced while the influence of primary ones is increased according to the attention mechanism. We used the advanced models in trajectory prediction as the comparison models, such as LSTM, support vector machine (SVM), back propagation (BP) neural network, Hidden Markov Model (HMM), and convolutional long-term memory neural network (CNN-LSTM). The model we proposed is superior to the model above based on quantitative analysis and comparison.

Using Reinforcement Learning to Improve Airspace Structuring in an Urban Environment

Current predictions on future drone operations estimate that traffic density orders of magnitude will be higher than any observed in manned aviation. Such densities redirect the focus towards elements that can decrease conflict rate and severity, with special emphasis on airspace structures, an element that has been overlooked within distributed environments in the past. This work delves into the impacts of different airspace structures in multiple traffic scenarios, and how appropriate structures can increase the safety of future drone operations in urban airspace. First, reinforcement learning was used to define optimal heading range distributions with a layered airspace concept. Second, transition layers were reserved to facilitate the vertical deviation between cruising layers and conflict avoidance. The effects of traffic density, non-linear routes, and vertical deviation between layers were tested in an open-source airspace simulation platform. Results show that optimal structuring catered to the current traffic scenario improves airspace usage by correctly segmenting aircraft according to their flight routes. The number of conflicts and losses of minimum separation was reduced versus using a single, uniform airspace structure for all traffic scenarios, thus enabling higher airspace capacity.

Optimization of Lateral Collision Risk of Aircraft Based on the Skid-Slip Event Model

With the development of aircraft flow under the existing airspace capacity now, the shortage of airspace resources and flight delays have become significantly severe. Therefore, building a safe and efficient mathematical model from quantitative analysis and improving the scientificity of route planning and management are essential propositions for future research on new navigation systems. Based on the traditional event collision risk assessment model, the collision module is upgraded to an ellipsoid according to the performance of aircraft, integrating the boundary curvature optimization characteristics of the TSRRT (task-space rapidly-exploring random trees) algorithm. An aircraft event lateral conflict resolution model based on the TSRRT algorithm is proposed. Taking the A320 aircraft and 737-800 aircraft as experimental subjects, the corresponding collision coefficients are imported into software tools for the simulation and the Kalman filtering is combined to verify the smoothness of the front and rear boundary curvatures. The result proves that the event side collision risk of aircraft based on TSRRT is 15% of the traditional event model and the smooth curvature error is reduced by 80% due to the improvement. Therefore, the improved event model is practical and valuable, providing a theoretical basis for future track-based operations.

Trajectory pattern identification and classification for real-time air traffic applications in Area Navigation terminal airspace

In order to address the continuing growth of demands on airspace capacity, various navigation methods have been developed such as Area Navigation (RNAV), which allows pilots and air traffic controllers to have a higher degree of freedom in the airspace, but at the same time, the airspace becomes more complex than ever, and maintaining the safety and efficiency becomes challenging. To develop assistant tools for such situation, this paper proposes (i) a trajectory pattern identification framework that can identify complex and diverse trajectory patterns in the RNAV terminal airspace, and (ii) a recurrent neural network-based real-time trajectory pattern classification framework that is necessary for real-time air traffic applications. The proposed frameworks are tested with the real air traffic data recorded at Incheon International Airport, South Korea, in 2019, and evaluated by predicting estimated time of arrival in a real-time manner.

Hyperheuristic Approach Based on Reinforcement Learning for Air Traffic Complexity Mitigation

Airspace capacity has become a critical resource for air transportation. Complexity in traffic patterns is a structural problem, whereby airspace capacity is sometimes saturated before the number of aircraft has reached the capacity threshold. This paper addresses a strategic planning problem with an efficient optimization approach that minimizes traffic complexity based on linear dynamical systems in order to improve the traffic structure. Traffic structuring techniques comprise departure time adjustment, en route trajectory deviation, and flight-level allocation. The resolution approach relies on the hyperheuristic framework based on reinforcement learning to improve the searching strategy during the optimization process. The proposed methodology is implemented and tested with a full day of traffic in the French airspace. Numerical results show that the proposed approach can reduce air traffic complexity by 92.8%. The performance of the proposed algorithm is then compared with two different algorithms, including the random search and the standard simulated annealing. The proposed algorithm provides better results in terms of air traffic complexity and the number of modified trajectories. Further analysis of the proposed model was conducted by considering time uncertainties. This approach can be an innovative solution for capacity management in the future air traffic management system.

The value of flexible flight-to-route assignments in pre-tactical air traffic management

In European air traffic management, there are discussions regarding the future role of the network manager (NM): in particular, should the NM be able to assign flights to specific trajectories, should airspace users be allowed to freely choose their preferred trajectory, or something in between? In this paper, we develop a modeling framework that can be adapted to these settings to assess their effect on key performance indicators.We focus on the pre-tactical stage of planning air traffic for a future departure day, meaning that airspace capacity budgets are given and incoming flight intentions need to be offered one or several ‘trajectory products’ for a (possibly dynamically determined) charge. These trajectory products differ in the amount of flexibility that they provide the NM to route the flight. Charges are set so as to reward greater flexibility of airspace users with lower charges. The airspace user chooses one of the offered trajectory products according to a choice model that reflects their preferences given, among others, the product charges. Shortly before the departure day, the NM decides simultaneously on the routing (within the limits defined by the purchased trajectory products) and on each airspace’s sector opening scheme (within the limits of the fixed capacity budgets) so as to minimize the overall displacement cost. Methodologically, the problem deviates from typical dynamic pricing problems in various major ways, e.g., featuring a boundary condition that we show to be NP-hard as well as fairness and revenue neutrality constraints. The problem is cast in the form of a dynamic program. We exploit a certain structure in the boundary condition to formulate an efficient heuristic. Based on a numerical case study, we find that the use of dynamically priced trajectory products achieves a cost performance close to the one obtained if the NM has a mandate to simply assign flights to trajectories. Therefore, this seems an attractive design for the role of the NM, giving airspace users some choice whilst achieving low overall costs.

Feasibility Study of Free Route Airspace over the North Pacific

A fast-time simulation study was conducted to examine the expansion of flexible track operations into the area occupied by the five North Pacific (NOPAC) air traffic service routes, an area of high traffic demand. In this study, user-preferred routes were created in a proposed NOPAC free route airspace (FRA) area, in which airspace users may design routes with few constraints and compared with current fixed NOPAC route structure by fast-time simulation. To reflect the effect of daily and seasonal wind variations, a clustering analysis approach was applied to select representative wind conditions for flight route generation. Fuel burn was used to evaluate the effect of the NOPAC FRA on the efficiency of flight operations, and potential loss of separation (PLOS) was examined for three minimum lateral separations to evaluate the effects of communication, navigation, and surveillance performance on airspace capacity and air traffic control workload. The simulation results show a trend of increased efficiency of individual NOPAC FRA flight routes, with potential mean fuel consumption savings of 849.2 kg for eastbound Alaska flights and 532.4 kg for westbound North America flights compared with current route configurations. Results of PLOS also show reduced overall PLOS time. This indicates that a NOPAC FRA could improve capacity and efficiency while maintaining or increasing safety.

RNN-CNN Hybrid Model to Predict C-ATC CAPACITY Regulations for En-Route Traffic

Meeting the demand with the available airspace capacity is one of the most challenging problems faced by Air Traffic Management. Nowadays, this collaborative Demand–Capacity Balancing process often ends up enforcing Air Traffic Flow Management regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictions.

Cross-border Free Route Airspace concept and its impact on flight efficiency improvement

The implementation of Free Route Airspace (FRA) in European Airspace has significantly reduced flight distances. However, there are still elements in the conventional FRA that need to be optimized in order to allow a further improvement in flight efficiency and these are the predefined entry and exit points that are still obligatory to be inserted in a flight plan by the crews and to be flew over during the flight. The introduction of cross-border FRA is another milestone towards the improvement of flight and Air Traffic Management (ATM) systems efficiency. The purpose of the article is to examine the possibility of implementation and expansion of already existing cross-border FRAs and how much distance would be saved if it was possible to fly in a cross-border FRA, instead of a conventional FRA. Thus, two possible scenarios on the extract of a chosen flight route, based on a real-world flight, operated by inter alia LOT Polish Airlines, are examined. Route distance extension was calculated using the Horizontal Flight Efficiency method. Subsequent calculations include potential flight time savings along with fuel waste, CO2 emission and operating costs. The validation of the results obtained through calculation was conducted on X-Plane 11 as a simulation of two examined scenarios, using a Boeing 737-800. The results of the calculations and simulations indicate that the implementation and expansion of cross-border FRA may have a positive impact on flight efficiency improvement due to the reduction of distance and consequently the flight time, fuel waste, CO2 emission and operating costs. It can also mitigate the problem of the European Airspace capacity gradually reaching its maximum level. Thus, there is still a lot of place for further optimization of air traffic flow in European Airspace.

Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

Synergetic trajectory planning of flights is one of the important goals of trajectory-based operation (TBO), and it is also a method to further improve the utilization of airspace resources with the increasing number of flights in recent years. In order to plan the four-dimensional trajectory (4DT) pre-tactically and comprehensively, match the flight traffic with airspace capacity, reduce congestion, potential conflicts, and fuel consumption thus improving the efficiency of flights, this paper conducts a method for synergetic trajectory planning in the en-route phase from the perspective of airlines. Firstly, the aircraft performance model, aircraft fuel consumption model, and atmospheric model are constructed according to the base of aircraft data (BADA3.11), and an airspace congestion prediction model is constructed based on the historical flow data of airspace. Secondly, a multi-objective synergetic trajectory planning model is established, and a solution method based on the non-dominated sorting genetic algorithm and simulated annealing algorithm (NSGA3-SA) for the problem of synergetic trajectory planning is designed. The simulation shows that the optimization model and the solution algorithm of NSGA3-SA can reduce fuel consumption by about 4.5% compared to the original flight plans and has a good effect on reducing congestion and avoiding conflicts. The running time of the NSGA3-SA can meet the operational requirements of the pre-tactical trajectory planning. The multi-objective optimization model and the solution algorithm proposed in this paper have great value for the research of flight plan optimization.

The effect of the Croatian Free Route Airspace implementation on the Air Traffic Complexity

It is well known that the increase in air traffic brings some negative connotations regarding delays, airspace capacity, air traffic controller workload, etc. To mitigate those problems, the European Commission has issued a development plan through the Single European Sky initiative, which yields a dozen solutions through the Single European Sky Air Traffic Management Research Joint Undertaking. Some of which is Free Route Airspace which enables aircraft operators to fly and plan direct routes through the airspace. Since this solution changes the way that the aircraft flies through airspace, different and new airspace routes have emerged. In this paper, the authors used fast time simulations with the PRU complexity model to observe how air traffic complexity was affected through Free Route Airspace implementation. It was observed that the increase in occupancy followed the increase in air traffic complexity, but not all results could be explained through the increase in the occupancy count. Also, to verify suspected claims the pre-FRA traffic from the 2016 was run on the FRA airspace and the reduction of complexity was observed. The authors concluded that the Free Route Airspace helped reduce the overall air traffic complexity, but further research and validation are still needed to concisely confirm those claims.

Capacity of a Constrained Urban Airspace: Influencing Factors, Analytical Modelling and Simulations

Capacity of a Constrained Urban Airspace: Influencing Factors, Analytical Modelling and Simulations

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs’ trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas representing safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.

Impact of Aircraft Performance and Time of the Day on Flight Arrival Delays Prediction in the United States: a Machine Learning Classification

The excessive growth of air traffic, with the limited airspace and airports capacity, results in a flight demand-capacity imbalance leading to air traffic delays. This paper explores the factors associated with delay in both microscopic and macroscopic ways. The aim is to develop a model which analyzes and predicts the occurrence of flight arrival delays using US domestic flight data for the year 2018. It will provide passengers, airlines and airport managers with reliable flight arrival schedules, and consequently reduce economic losses and enhance passengers trust. Beside database features, the proposed model is to the best of our knowledge the first attempt to predict flight arrival delays using three new features which are contributive factors to delays: Departure Time and Arrival Time of the day in which the flight was performed (Early morning, late morning, noon, afternoon, evening or night) and model of aircraft. Four Machine Learning classifiers namely Random Forest, Decision Trees, K-Nearest Neighbors and Naive Bayes were used. In order to find the best parameters of each algorithm, we implemented Grid Search technique. The performance of each classifier was compared in terms of hyperparameters tuning, classification metrics and features description. The experimental results showed that the proposed system was able to predict flight arrival delays with the best Random Forest accuracy of 0.9356 and a higher number of correctly classified flights. To prove the importance of our findings, we compared our model to that of existing literature studies.

Data-Driven Analysis for Calculated Time Over in Air Traffic Flow Management

Excessive air traffic demand saturates air traffic control and causes traffic delays due to limited airspace and airport capacity. Air traffic flow management (ATFM) is used to regulate such demand using traffic management initiatives (TMIs), such as ground delay, miles-in-trail and speed adjustment. The Calculated Time Over (CTO), which is a time-assignment TMI for an in-flight aircraft, is being widely developed and CTO operational trials have also been conducted in Japan. This study develops means of estimating several important indices, including the compliance rate and mean expected delay, using data collected in the trial and shadow CTO operations. The estimation methods show variation in indices with differing maximum assigned CTO, while the analytical results suggest the optimal maximum assigned CTO. Furthermore, a variable maximum assigned CTO is proposed to improve the CTO operation. The analytical results show that the proposed method can improve the compliance rate as well as the mean expected delay.

Critical Links Detection in Spatial-Temporal Airway Networks Using Complex Network Theories

In an airway network, some critical links exist that are vital for the structural integrity and performance of the network. The detection of such links may assist with improving the imbalance between the limited airspace capacity and the ever-increasing traffic demand, which elicit flight delays, significant economic losses, etc. However, it is challenging to identify such links as they evolve (both in space and time) with changing traffic flow dynamics. This paper proposes a complex network approach for spatial-temporal critical links detection in a given airway network. First, flight track data is employed to characterize the airway network as weighted spatial-temporal networks. Then edge centrality and network percolation metrics are adopted to detect the critical links in each snapshot of the spatial-temporal networks. Afterward, the critical links detected by the two metrics are spatially overlapped to determine the final critical links over time. To examine the operational validity of the proposed method, we carry out a case study on the Southeast Asia airway network derived from one-month flight track data. Results demonstrate that the spatial distribution of the critical links varies over different traffic scenarios, and most of the identified critical links are found in the transition sectors with complex traffic situations. Four links, which are parts and at crosses of major trunk airways connecting to major navigation aids (VOR/DME) in the studied network, appear highly in all examined traffic scenarios. The unavailability of such links may lead to traffic flow disruptions. Observations by subject matter experts from air-traffic data visualizations demonstrate that the complex network-based methods can dynamically identify airway links that are operationally critical under time-evolving air-traffic scenarios. With good traffic flow prediction tools in the future, this method can be adopted to predict critical links in airway networks to better assist controllers in real-time air traffic management.

Central Authority–Controlled Air Traffic Flow Management: An Optimization Approach

Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%–3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM.

Scanning the Issue

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Two-Stage Scalable Air Traffic Flow Management Model Under Uncertainty</b> <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>G. G. N. Sandamali, R. Su, K. L. K. Sudheera, Y. Zhang, and Y. Zhang</i> <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In order to efficiently balance the current and future air traffic demands with the system capacity, a proper air traffic flow management (ATFM) approach is required. The current focus of ATFM is generally on optimally utilizing the available airspace and airport capacities while maintaining the required safety separation between aircraft. Yet, only a minor focus is given to the inherent uncertainty in the air transportation system (ATS), especially to its adverse effect on safety and day-to-day operations. To this end, the authors propose an ATFM framework for scrutinizing the stochastic nature of ATS through a chance-constraint-based probabilistic approach. Moreover, anticipating the high volumes in air traffic in the future, the authors propose to split the model into two stages, in which the first stage scrutinizes the behavior of a set of flights as a flow, while the second stage transforms them into individual flight plans, enhancing scalability. The two models are formulated as an integer linear programming (ILP) problem, and a mixed-integer linear programming (MILP) problem at stages I and II, respectively. The NP-hard nature of the overall problem is minimized by transforming the problem into a maximum weighted independent set (MWIS) finding problem.

Efficient and fair traffic flow management for on-demand air mobility

The increased use of drones and air-taxis is expected to make airspace resources more congested, necessitating the use of unmanned aircraft systems traffic management (UTM) initiatives to ensure safe and efficient operations. Typically, strategic UTM involves solving an optimization problem that ensures that proposed flight schedules do not exceed airspace and vertiport capacities. However, the dynamic nature and low lead-time of applications such as on-demand delivery and urban air mobility traffic may reduce the efficiency and fairness of strategic UTM. We first discuss the adaptation of three fairness metrics into a traffic flow management problem (TFMP). Then, with computational simulations of a drone package delivery scenario in Toulouse, we evaluate trade-offs in the TFMP between efficiency and fairness, as well as between different fairness metrics. We show that system fairness can be improved with little loss in efficiency. We also consider two approaches to the integrated scheduling of both high lead-time flights (i.e., flights with a schedule known in advance) and low lead-time flights in a rolling horizon optimization framework. We compare the performance of both approaches for different horizon lengths and under varying proportions of high and low lead-time flights.

Flexibility in strategic flight planning

A deterministic model that indicates flexibility of flights at the strategic level (up to 6 months ahead) taking into account changing airspace configurations and capacity is formulated. Flexibility is quantified by means of time windows (TWs). Flights complying with TWs guarantee that they will not impact negatively any other flight. Three variants of the model and three types of TWs are tested on a large-size data instance (the European network for an entire day of traffic). The model output specifies the constrained flights (i.e., with TWs shorter than the maximum size allowed for their definition), the constraining sector-hours and provides a list of saturated sector-hours. The meaning of each of the results is explored, across the three TW model variants, as well as the capability of the model variants to assure that capacity limits will not be exceeded. The criticality index, a measure of the sector-hour saturation, is introduced. This index can be used to identify areas for potential improvements. Sharing the information obtained from the TW model results at a strategic level can help both airlines and air navigation service providers (ANSPs) to improve the network status: airlines can decide to re-route heavily constrained flights (e.g., with one minute wide TWs), whereas ANSPs could decide to re-organise the capacity provision of the saturated airspace portions. The TW model can be re-run with the proposed changes, with the goal to assess the impact on both the individual stakeholders and the network. Thus, the model offers the measure of flight flexibility, and can be used as a tool to assess the impact of changes, helping in decision-making processes of airlines and ANSPs.