Air Traffic Flow Research Articles

Sensitivity analysis of weather research and forecasting (WRF) model output variables to the thunderstorm lifecycle and its application

Accurate and timely forecasts of thunderstorms at lead times of more than 6 h can greatly improve the efficiency of air traffic flow management. To achieve this goal, thunderstorms occurring at Urumqi International Airport in 2020 were investigated using Weather Research and Forecasting (WRF) model output variables. An attempt was made to study the sensitivity of WRF output variables to the different stages (cumulus, mature, and dissipating) of the thunderstorm lifecycle. The variables considered in this paper include the wind speed (WSPD), composite radar reflectivity, echo top height, convective available potential energy (CAPE), convective inhibition (CIN), and lift index (LI). It was found that CIN is the most sensitive to an approaching thunderstorm. WSPD is extremely sensitive to the thunderstorm occurrence, closely followed by CIN. CAPE, CIN, and LI are all sensitive to dissipating thunderstorms. To improve thunderstorm forecasts, a simple and practical objective thunderstorm forecasting method, i.e., the thunderstorm probability (TSP) forecasting method, based on the combination of the above-mentioned variables, was proposed. Comparison of objective TSP forecasts and manual subjective forecasts indicated that TSP forecasts performed much better than did manual forecasts over the summer period of 2020.

System Design and Engineering Implementation of Vehicle Transponder for A-SMGCS

Global aviation transport industry has entered the stage of rapid development, and the air traffic flow is exploding at an incredible rate. The Asia-Pacific region is the core of the future growth of the aviation industry. The growth of the fleet is expected to reach 50%, especially in China. Nearly 4,000 new aircrafts are expected to be delivered in the next 10 years, with the fleet increasing by more than 100%. The practitioners of modern aviation regard ensuring aviation safety and improving the efficiency of aviation operations as two important themes to promote the surge development of aviation. To solve the effective surveillance, guidance, and control of flights and vehicles operating on the surface in complex airports, the global airports are recommending Advanced-Surface Movement Guidance and Control System (A-SMGCS) to realize the effective management of the airport scene and improve the safety of the surface operational capability. Airport surface security management objectives include aircraft, vehicles, and other moving targets, almost all aircraft has an operative transponder, and the vehicles need the similar function transponder to indicate its identification in A-SMGCS, so it can be guided effectively by a given procedures. This paper introduces a vehicle transponder system design from appearance structure and electrical performance and implements engineering modification on a vehicle. In an airport with A-SMGCS, two methods of fixed reference point and manoeuvre test are selected, and the performance indexes of the transponder are recorded and compared. Finally, it is concluded that the transponder meets the performance parameters specified by FAA technical standards; it can be used as a qualified vehicle transponder to provide vehicle positioning information for the A-SMGCS system.

Plithogenic multi‐criteria decision making approach on airspace planning scheme evaluation based on ATC‐flight real‐time simulation

Airspace planning scheme evaluation based on ATC-Flight real-time simulation which incorporates air traffic control simulator and flight simulator has become increasingly important. It has irreplaceable advantages of truly simulating and in-depth evaluating airspace operation, especially for analyzing human factors. As a multi-criteria decision making (MCDM) problem, it poses challenges which this study aims to overcome from three aspects. Firstly, the complexity of airspace and the characteristics of ATC-Flight real-time simulation are analyzed to build a multi-criteria evaluation system. The criteria include air traffic flow, airspace operational performance, flight procedure quality, cost, controller workload and pilot workload. Secondly, a novel weighting method based on plithogenic set is presented to derive robust subjective criteria weights from a group of human decision makers. The subjective weights are then combined with objective weights using game theory to determine cumulative criteria weights. Thirdly, technique for order preference by similarity to ideal solution (TOPSIS) and grey correlation analysis are integrated to perform comprehensive evaluation and ranking. The first series of ATC-Flight real-time simulations in China are conducted to illustrate a case study. The results demonstrated that the proposed MCDM approach could improve the accuracy and discriminability of evaluation results, and outperformed other existing well-known MCDM algorithms.

A Safety-Aware Real-Time Air Traffic Flow Management Model Under Demand and Capacity Uncertainties

Inherent uncertainties of the air transportation system (ATS) can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, etc.. Current air traffic flow management (ATFM) models rarely consider both demand and capacity uncertainties in their algorithms, and generally focus on minimizing the flight delays under deterministic constraints. Thus, to bridge this gap, we propose a framework for en-route ATFM while scrutinizing uncertainties in en-route capacity and demand and their imbalance, via a chance constraint based probabilistic approach. The proposed framework plays a key role in ensuring the safety of the overall ATS in terms of maintaining the safety separation between flights and constraining the capacity of the sectors as well. Moreover, flight level assignments scheme is proposed based on the Base of Aircraft Data (BADA) of the European Organization for the Safety of Air Navigation (EUROCONTROL) with the objective of minimizing the fuel consumption. The model further minimizes the overall expected delay of the system using the control actions of ground holding, speed control, rerouting, and flight cancellations. At the implementation stage, two phases of ATFM as pre-tactical and tactical are considered, in which the former focuses on generating optimal trajectories and the latter focuses on real-time updates of flight plans. The computational complexity is reduced by shrinking the feasibility region and decomposing the problem into maximum weighted independent sets. The experimental results of realistic large-scale problems demonstrate the effectiveness and computational feasibility of our ATFM framework.

Characterization of the terminal area of standard arrival routes using the queueing system

The article solves the problem of determining the probabilistic characteristics of the STARs legs which specify a sequencing technique of “point merge” and “trombone”-type considering the intensity of the air traffic flow and the formation of a queue. This task is closely related to the airspace efficiency, as well as to the limit values of flight safety performance. An application of the queueing system makes it possible to optimize the elements of the airspace structure on an objective basis, and in the event of considering the inverse problem to establish limit values for the air traffic flow characteristics. The characteristics of the “point merge” and “trombone”-type schemes become of prime importance in the air hub, where several airfields function of each other in a relatively small volume of airspace – as it happens in the Moscow air hub, so it is the given hub that is taken as a practical example. In this problem the basic model is the queueing system with limited-size queues where the optimal number of service channels in a stationary air traffic flow is determined in a probabilistic way. The stated model encapsulates the essence of the “trombone” or “point merge”-type scheme, where the number of service channels corresponds to the number of flight levels on the arc of the “point merge” or on the horizontal flight segment of the “trombone”. Now, the number of such flight levels, as a rule, corresponds to the number of standard arrival routes involved in the formation of a “trombone” (“point merge”), which, from a practical point of view, is excessive. The task of using the mathematical apparatus of the queueing system is to determine the optimal number of flight levels – service channels of the model while establishing the required probability of its failure. As a mathematical model of the queueing system, the “trombone”-type scheme is used, and in the mentioned above example, the structure of the airspace is presented using the “point merge”-type scheme as a regulator of the aircraft sequence for landing. All the computations are performed for the certain intensity of air traffic flow for a specified airfield, considering the full-scale application of continuous descent operations and continuous climb operations (CDO, CCO). As a result of solving the problem, the value for the optimal number of flight levels on the “trombone” or “point merge”-type scheme was obtained, and the dependence of the number of service channels (flight levels on the “trombone” or “point merge”) on the value of the given probability of the queueing system failure was shown. The proposed approach to the airspace structure formation has prospects for implementation.

Explaining deep reinforcement learning decisions in complex multiagent settings: towards enabling automation in air traffic flow management.

With the objective to enhance human performance and maximize engagement during the performance of tasks, we aim to advance automation for decision making in complex and large-scale multi-agent settings. Towards these goals, this paper presents a deep multi agent reinforcement learning method for resolving demand - capacity imbalances in real-world Air Traffic Management settings with thousands of agents. Agents comprising the system are able to jointly decide on the measures to be applied to resolve imbalances, while they provide explanations on their decisions: This information is rendered and explored via appropriate visual analytics tools. The paper presents how major challenges of scalability and complexity are addressed, and provides results from evaluation tests that show the abilities of models to provide high-quality solutions and high-fidelity explanations.

A Clustering Ensemble Method of Aircraft Trajectory Based on the Similarity Matrix

Performing clustering analysis on a large amount of historical trajectory data can obtain information such as frequent flight patterns of aircraft and air traffic flow distribution, which can provide a reference for the revision of standard flight procedures and the optimization of the division of airspace sectors. At present, most trajectory clustering uses a single clustering algorithm. When other processing remains unchanged, it is difficult to improve the clustering effect by using a single clustering method. Therefore, this paper proposes a trajectory clustering ensemble method based on a similarity matrix. Firstly, a stacked autoencoder is used to learn a small number of features that are sufficiently representative of the trajectory and used as the input to the subsequent clustering algorithm. Secondly, each basis cluster is used to cluster the data set, and then a consistent similarity matrix is obtained by using the clustering results of each basis cluster. On this basis, using the deformation of the matrix as the distance matrix between trajectories, the agglomerative hierarchical clustering algorithm is used to ensemble the results of each basis cluster. Taking the Nanjing Lukou Airport terminal area as an example, the experimental results show that integrating multiple basis clusters eliminates the inherent randomness of a single clustering algorithm, and the trajectory clustering results are more robust.

On the binary formulation of air traffic flow management problems

We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions.

A Review of Hydrostatic Bearing: Research and Analysis

Background: As a kind of sliding bearing with liquid as a lubricant, Hydrostatic journal bearing can be shown from tribology, mechanics, optimization method and structural design in engineering practice. Hydrostatic bearing characterized by high precision, high rigidity, and long life has drawn people's attention and been widely adopted. As progress has been made in science and technology, people will have a better understanding of the principles and characteristics of the hydrostatic journal bearing. Objective: The development of hydrostatic bearing in recent years is studied and analyzed. Methods: More than 70 recently published patents and journal and conference papers related to the developments in air traffic flow management are reviewed, and the outcomes in temperature field, pressure field, deformation, lubrication, bearing capacity and friction are studied. Results: Through these research results, the current research status of hydrostatic bearings is summarized, and the three aspects of flow field characteristics, lubrication characteristics and tribology are explained, and solutions and feasibility measures for existing outstanding problems in each aspect are mentioned. Conclusion: Based on this article, the design and optimization of hydrostatic bearings are of guiding significance. Finally, the current problems and challenges of hydrostatic bearings are analyzed, and future research directions and research priorities are prospected.

Predicting Air Traffic Density in an Air Traffic Control Sector

Air traffic over Indian skies continues to increase rapidly and due to this increased demand, efforts must be made to efficiently balance demand with capacity. Air traffic flow management is a concept that allows for better demand and capacity management. It helps airlines plan routes and schedule flights. The ability to estimate the volume of air traffic is hence a critical element in flow management. Determining traffic in an ATC sector necessitates a thorough examination of various hidden parameters. The prediction is carried out using machine learning algorithms, Recurrent Neural Network, and Long Short Term Memory that examine the data and find a pattern which then anticipates the traffic in the sector at any given moment in the near future. Given the real-time data and experimental findings from this study, it is clear that Long Short Term Memory provides a clearer indicator of the study's goal.

Understanding the Terminal Area Traffic Flow Characteristics Using Flight Record Data

Terminal area (TMA) is a significant component of the air transportation and air traffic management system. TMA consists of the airport and the transitional airspace between airports and air traffic control sectors and plays a non-trivial role in determining throughput and operational efficiency. Flights in TMA fly through defined courses, distances, and altitudes between specified navigation aids, expressed as standard terminal arrival routes and standard instrument departures. TMA operations are intrinsically complex, and a deeper understanding of TMA operational behaviour and performance is essential to provide timely solutions for schedule and slot allocations, flow, and congestion management. This paper proposes a flight record driven descriptive framework for simplified analysis and characterization of air traffic flows in TMA. This framework aims to perform a cross-sectional descriptive analysis of four key flow performance metrics and proposes a three-step methodology to extract these parameters from flight records. The framework is easy to comprehend, and results can contribute to developing new strategies and decision support tools for TMA traffic management.

Interactive trajectory modification and generation with FPCA

Moving object analysis is a constantly growing field with numerous concrete applications in terms of traffic understanding, prediction and simulation. While many algorithms and analytic processes exist, there are still areas of investigation with novel trajectory analysis methods. As such, the geometric information analyses data with respect to its statistical distribution along extracted dimensions. This opens new ways of gaining a better understanding of large and complex trajectory data sets while providing flexible data manipulations. In this paper, we report our investigations with the development of an interactive methodology based on the geometric information analytic process where users can analyze trajectories sets, cluster and deform them maintaining the actual statistical properties of the investigated trajectories. As a contribution, this paper shows how Functional Data Analysis can provide novel support for trajectory analyses taking into account the statistical properties of the investigated clusters. We also provide recommendations for efficient usage of the process, considering trajectory registration, initial clustering, trajectory deformation and generation. These recommendations are illustrated with actual examples validated by a domain expert of air traffic flow analysis.

Long Range Air Traffic Flow Management with Flight-Specific Flight Performance

The increasing need for dynamic in-flight adjustments of a trajectory allows the airport, air traffic control and the airline a high degree of flexibility in terms of in-flight execution. This concept enables numerous optimisation options to jointly meet the requirements of sustainable air transport to increase economic and ecological efficiency, as well as safety. One promising measure is to control the aircraft arrival rate to prevent over-demand in the approach sector around the airport. In so-called Long-Range Air Traffic Management, the arrival time of long-haul flights, in particular, is already controlled many hours before arrival. However, the control options and their effects on arrival time and fuel burn are heavily dependent on flight performance and the (hardly predictable) influence of the weather. In this study, we optimize the arrival time of 26 long-haul flights in the Asia-Pacific region with arrival at Changi Airport within a peak hour considering the arrival rate of medium-haul and short-haul flights. This control is done by speed adjustments and by choosing alternative routes. For the first time, we model each long-haul flight and its control options individually in real weather conditions. We found that speed adjustments should start three to four hours before arriving at the approach sector to maximize the fuel-saving potential of small deviations from the optimal cruising speed, considering the predictability of the arrival time under real weather conditions. Allowing the aircraft to additionally choose an alternative lateral route, different from the filed flight plan, both maximizes the potential for harmonization of the number of aircraft in the approach sector and minimizes the total fuel burn. Unlike speed adjustments, alternative routes changes are effective even during the last hour of the cruise phase.

Intelligent Management of Air Traffic Flow Based on Intelligent Motion Coordinate Data Model

To adapt to the intelligent management of traffic flow in modern aviation, this study improves the algorithm according to the data exercise method and establishes the mathematical model and vector model of intelligent motion coordinates. Moreover, according to the simulation test method, this study analyzes the six-degree-of-freedom equation of motion in the coordinate system of the civil aviation aircraft and uses the simulation method to regard the civil aviation as a rigid body with a certain mass and mass distribution. In addition, this study builds an intelligent model based on the demand analysis, and according to the actual demand, this study combines the simulation method to analyze the structure of the intelligent management model of air traffic flow. Finally, this study uses the experimental analysis method to verify the effect of the model proposed in this study and combines the experimental research to verify that the model proposed in this study has a certain effect.

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.

Air Traffic Flow Pattern Recognition and Analysis in Terminal Area Based on the Geodesic Distance

Air Traffic Flow Pattern Recognition and Analysis in Terminal Area Based on the Geodesic Distance

A n − D ant colony optimization with fuzzy logic for air traffic flow management

A n − D ant colony optimization with fuzzy logic for air traffic flow management

Influence of airspace avoidance due to political and safety issues on flight efficiency and environment

In the history of aviation there were many cases that led to the obligation or necessity to bypass certain airspaces, mainly due to political and safety reasons. One of the latest events is the incident over Belarusian airspace. The fundamental concept of the article is to examine the effects of the necessity to avoid certain airspaces by the operators, using the incident over Belarusian airspace as an example. Based on the observations of air traffic flow over affected region, route distance extension was calculated using the Horizontal Flight Efficiency method. Subsequent calculations include potential increase in flight time, fuel consumption, carbon dioxide emission and operating costs. The results were extrapolated to yearly amounts. based on the air traffic prediction reports. The air traffic prediction analysis as well as the results of the calculations and simulations indicate that the bypass of the airspaces on flight routes important to global economy, has a serious impact on the environment and operators’ budget due to increased aircraft’s fuel consumption resulting in growth of carbon dioxide emissions as well as operating costs. It also leads to deterioration in the European Airspace’s capacity, causing the necessity for Air Traffic Management units to reorganize the air traffic flow over the affected regions. Potential solutions mitigating the problem were also proposed.

Factors Impacting Chinese and European Vertical Fight Efficiency

Increasing complexity due to a constantly growing number of target functions turns air traffic trajectory optimization into a multidimensional and nonlinear task that in turn necessitates a focus on the case-sensitive most important criteria. The criteria vary by continent and involve operational, economic, environmental, political, and social concerns. Furthermore, the requirements may alter for a single flight along its journey since air traffic is a transcontinental, segment-wise differently affected transportation mode. Tracked flight data allow for the observation and evaluation of large numbers of flights, as well as the extraction of criteria relevant to flight efficiency and to derive optimization strategies to improve it. In this study, flight track data of China and Europe were compared toward flight efficiency. We found major disparities in both continents’ routing structures. Historical ADS-B data considered to be reference trajectories were assessed for flight efficiency while putting a dedicated focus on the vertical profile. Criteria to optimize vertical flight efficiency (VFE) were derived. Based on the findings, suggestions for improvement towards trajectories with minimum fuel are formulated. Different optimization strategies were tested to identify important input variables and, if possible, to determine differences between operation in China and in Europe. On average and in both regions, the influence of weather (e.g., wind speed and wind direction) exceeds the influence of aerodynamics (aircraft type, mass), as the weather-optimized vertical profile more often results in minimum fuel consumption than the aerodynamically optimized trajectory. Atmospheric conditions, network requirements, aircraft types and flight planning procedures are similar in China and Europe and only have a minor impact on flight efficiency during the cruise phase. In a multi-criteria trajectory optimization of the extracted reference trajectories considering the weather, operational constraints and prohibited areas, we found that in China, on average, just under 13% fuel could be saved through optimal vertical and horizontal routing. In Europe, the figure is a good 10%. Furthermore, we calculated a fuel-saving potential of 8% in China and 3% in Europe through vertical adjustments of the trajectory alone. The resultant reference trajectories will be used for further analysis to increase the efficiency of continental air traffic flows.

Dynamic sector characterisation model with the application of machine learning techniques

The ATC service has the objective of controlling airspace operations safely and efficiently. This control is becoming more and more difficult due to the increasing complexity of airspace. With the objective of collaborating and facilitating the provision of the control service, FLUJOS project aims to develop a methodology to characterise ATC sectors according to their complexity. This paper shows the first results obtained in this project. A methodology is proposed that first performs a statistical analysis of the data present in the flight plans of individual aircraft. The statistical analysis will be used to estimate the impact of air traffic flows. With this, the complexity of ATC sectors will finally be determined. In addition, a machine learning tool will be added to develop a dynamic methodology. After evaluating the methodology with data from Spanish airspace in 2019, it can be said that the results obtained are logical from an operational point of view, and that they allow a fairly accurate classification of the sectors based on their complexity. However, the proposed methodology is still a preliminary version, so more work will have to be done to add variables to achieve the objective of obtaining an even more accurate and realistic classification.