SESAR:The Past,Present,and Future of European Air Traffic Management Research

The Single European Sky Air Traffic management(ATM)Research(SESAR)project is the technological pillar of the European Commission’s Single European Sky Initiative to modernize ATM.Here,we describe the process of establishing SESAR and the main parts of the project:the research and development(R&D)part,which is led by the SESAR Joint Undertaking;the deployment part,which is managed by the SESAR Deployment Manager;and the European ATM Master Plan,which collects and lays out both the R&D and deployment needs.The latest European ATM Master Plan was adopted just prior to the current pandemic.The huge loss in air traffic due to the pandemic,and the speed of the recovery of the aviation industry will require reprioritization,but the main elements that have been established-particularly those in support of the environment-remain valid.

Evolution of Air Traffic Management Concept of Operations and Its Impact on the System Architecture

The air traffic management system(ATM)has the task of ensuring safe,orderly and expeditious flow of air traffic.The ATM system architecture is very much dependent on the concept of operations(ConOps).Over the years the evolution in ConOps has resulted in changes in the ATM′s physical architecture,improving its physical infrastructure,increasing the levels of automation and making operational changes to improve air traffic flow,to cope with increasing demand for air travel.However,what is less clear is the impact of such changes in ConOps on the ATM′s functional architecture.This is vital for ensuring optimality in the implementation of the physical architecture components to support the ATM functions.This paper reviews the changes in the ConOps over the years,proposes a temporally invariant ATM functional model,and discusses some of the main key technologies expected to make significant improvements to the ATM system.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Identification of Information-Seeking Behaviors from Air Traffic Controllers′ Eye Movements

Air traffic controllers are the important parts of air traffic management system who are responsible for the safety and efficiency of the system.They make traffic management decisions based on information acquired from various sources.The understanding of their information seeking behaviors is still limited.We aim to identify controllers′ behavior through the examination of the correlations between controllers′eye movements and air traffic.Sixteen air traffic controllers were invited to participate real-time simulation experiments,during which the data of their eye ball movements and air traffic were recorded.Tweny-three air traffic complexity metrics and six eye movements metrics were calculated to examine their relationships.Two correlational methods,Pearson′s correlation and Spearman′s correlation,were tested between every eye-traffic pair of metrics.The results indicate that controllers′two kinds of information-seeking behaviors can be identified from their eye movements:Targets tracking,and confliction recognition.The study on controllers′ eye movements may contribute to the understanding of information-seeking mechanisms leading to the development of more intelligent automations in the future.

Ontology-Based Approach for Human Competency Gap Analysis in Air Traffic Management:A Case Study of Georgia

The Global Air Navigation Plan is a flexible global engineering approach that allows all States to advance their Air Navigation capacities based on their specific operational requirements.Aviation professionals have an essential role in the transition to,and successful implementation of the GANP.The research work is focused on the creation of methodology for the partial automation of the comparison competences of Air Traffic Management(ATM)personal and synthesis of training courses and modules,using a formal,ontology-based approach as a tool to solve these problems.One of the problems in the implementation of the GANP is that,on the one hand,there are currently no unified requirements for all categories of ATM personnel,and on the other hand,the development of ATM technologies is far ahead of the pace of training of personnel of appropriate qualifications.This problem becomes even more noticeable in countries that have just started an active modernization of ATC systems and do not have enough experience in this field.The paper describes the general methodological approach based on the education ontology modelling for human competency gap analysis in ATM and for gap analysis between the university curricula outcomes and the ATM requirements.The ontology of key personnel competencies issues for the design and integration of large-scale future ATM programmes is proposed.

Trajectory clustering for arrival aircraft via new trajectory representation

Trajectory clustering can identify the flight patterns of the air traffic,which in turn contributes to the airspace planning,air traffic flow management,and flight time estimation.This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation.The proposed method consists of four significant steps:representing the trajectories,grouping the trajectories based on the new representation,measuring the similarities between different trajectories through dynamic time warping(DTW)in each group,and clustering the trajectories based on k-means and densitybased spatial clustering of applications with noise(DBSCAN).We take the inbound trajectories toward Shanghai Pudong International Airport(ZSPD)to carry out the case studies.The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns,but also improve the performance of flight time estimation.

Analysis on Potential Conflict Frequency of Intersected Air Routes in Terminal Airspace Design

In order to obtain accurate conflict risks in terminal airspace design,the concept and calculation model of potential conflict frequency for intersected routes are proposed.Conflict frequency is represented by the product of horizontal conflict frequency and vertical conflict probability.The horizontal conflict frequency is derived from the probability density distribution of conflicts in a period of time.Based on the recorded radar trajectory data,the concept and model of ROUTE distance are proposed,and the probability density function of aircraft height at a specified ROUTE distance is deduced by kernel density estimation.Furthermore,vertical conflict probability and its horizontal distribution are achieved.Examples of three intersected arrival and departure route design schemes are studied.Compared with scheme 1,the conflict frequency values of the other two improved schemes decrease to53% and 24%,respectively.The results show that the model can quantify potential conflict frequency of intersected routes.

A Combined Arrival and Departure Scheduling for Multi-airport System

A combined arrival and departure scheduling problem is investigated for multi-airport system to alleviate the problem of airspace congestion and flight delay.Firstly,the combined scheduling problem for multi-airport system is defined through in-depth analysis of the characteristics of arrival and departure operations.Then,several constraints are taken into account,such as wake vortex separation,transfer separation,release separation,and separation in different runway operational modes.Furthermore,the scheduling model is constructed and simulated annealing algorithm is proposed by minimizing the total delay.Finally,Shanghai multi-airport system is chosen to conduct the simulation and validation.And the simulation results indicate that the proposed method is able to effectively improve the efficiency of arrival and departure operations for multi-airport system.

Dynamic reconfiguration for en-route airspace

As the air traffic demand is anticipated to be increased significantly in the near future,dynamic and effective allocation of the airspace resource is becoming a world-wide focus in the research field of air traffic management（ATM）.Taking the U.S.targeting the en-route airsapce,a dynamic airspace configuration（DAC） algorithm to reconfigure the airspace in consideration of higher efficiency and safety is presented.First,a modeling technique based on graph theory is proposed to generate a mathematical model for the airspace,and then,the graph model is partitioned into subgraphs for the purpose of sectorizatoin.The final step generates sector configuration with desirable geometry shape.Through analysis on the Cleveland airspace center（ZOB） in the U.S.,the algorithm is proved to be robust to time-varying traffic load.

Sectorization Model of Terminal Airspace with Arrival and Departure Separation

Terminal airspace(TMA)is the airspace centering several military and civil aviation airports with complex route structure,limited airspace resources,traffic flow,difficult management and considerable airspace complexity.A scientific and rational sectorization of TMA can optimize airspace resources,and sufficiently utilize the control of human resources to ensure the safety of TMA.The functional sectorization model was established based on the route structure of arriving and departing aircraft as well as controlling requirements.Based on principles of sectorization and topological relations within a network,the arrival and departure sectorization model was established,using tree based ant colony algorithm(ACO)searching.Shanghai TMA was taken as an example to be sectorizaed,and the result showed that this model was superior to traditional ones when arrival and departure routes were separated at dense airport terminal airspace.

Identifying and managing risks of AI-driven operations:A case study of automatic speech recognition for improving air traffic safety

In this work,the primary focus is to identify potential technical risks of Artificial Intel-ligence(AI)-driven operations for the safety monitoring of the air traffic from the perspective of speech communication by studying the representative case and evaluating user experience.The case study is performed to evaluate the AI-driven techniques and applications using objective metrics,in which several risks and technical facts are obtained to direct future research.Considering the safety–critical specificities of the air traffic control system,a comprehensive subjective evaluation is conducted to collect user experience by a well-designed anonymous questionnaire and a face-to-face interview.In this procedure,the potential risks obtained from the case study are confirmed,and the impacts on human working are considered.Both the case study and the evaluation of user experience provide compatible results and conclusions:(A)the proposed solution is promising to improve the traffic safety and reduce the workload by detecting potential risks in advance;(B)the AI-driven techniques and whole diagram are suggested to be enhanced to eliminate the possible distraction to the attention of air traffic controllers.Finally,a variety of strategies and approaches are discussed to explore their capability to advance the proposed solution to industrial practices.

INTELLIGENT SIMULATION FOR ALTERNATIVES COMPARISON AND APPLICATION TO AIR TRAFFIC MANAGEMENT

We present a simulation run allocation scheme for improving efficiency in simulation experiments for decision making under uncertainty. This scheme is called Optimal Computing Budget Allocation (OCBA). OCBA advances the state-of-the-art by intelligently allocating a computing budget to the candidate alternatives under evaluation. The basic idea is to spend less computational effort on simulating non-critical alternatives to save computation cost. In particular, OCBA is employed to intelligently provide the smallest number of simulation runs for a desired accuracy. In this paper, we present a new and more general OCBA scheme which can consider cases that users are interested not only the best design, but also any one in a good design set. In addition, this paper also presents the application of our OCBA to a design problem in US air traffic management. The national air traffic system in US is modeled as a large, complex, and stochastic network. The numerical examples show that the computation time can be reduced by 54% to 88% with the use of OCBA.

A new skeleton based flying bird detection method for low-altitude air traffic management

In low-altitude air traffic management, non-cooperation targets are the greatest threat to security of low-flying aircraft. Among various aviation fatalities, flying bird is the main factor with the highest risk and directs economic losses amounted to nearly 10 billion US dollars each year.Therefore, Flying Bird Detection(FBD) has attracted considerable attention in low-altitude air traffic management. In this paper, we propose a skeleton based FBD method via describing bird motion information with a set of key poses. To overcome the variability of birds, the skeleton feature is selected as a relatively fixed and common characteristic for the pose appearance of flying bird. Based on the geometric topology among some key parts of bird body, a set of key poses can be described by some extracted skeleton features, which are used to represent the bird motion information. Aimed at robustly handling with the pose variations, multiple pose-specific classifiers are individually trained to learn the representative poses of the flying bird. At the detection stage,the flying bird skeleton features are combined with extracted key-pose sets to perform the flying bird classification task from each image. Afterwards, the key-frame pose-change set and the consistency of the classification results from sequent images are employed to validate the final detection results.Experiments on flying bird datasets demonstrate the effectiveness and efficiency of the proposed method.

Characterizing Flight Delay Profiles with a Tensor Factorization Framework

In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.

Secondary trading of airport slots:Issues and challenges

Most Level 3 airports around the world suffer severe congestion and flight delays.Airlines have to obtain airport slots in order to schedule flights at such airports.The main way for airlines to acquire slots is primary slot allocation,in which a slot coordinator distributes slots to airlines according to certain rules and regulations.Due to excessive demand for slots and restrictions on allocation rules,it is difficult for some airlines to obtain the desired slots in this manner.Another way for airlines to obtain slots is through secondary slot trading,in which slots can be redistributed among airlines without being returned to slot pool.The secondary trading of airport slots has played a positive role in promoting the efficient utilization of slot resources,but systematic studies are insufficient.This paper discusses the reasons for the existence of slot secondary trading,sorts out the main policies and rules governing the mechanism,investigates its impacts on the industry and society,and points out the major problems and challenges.The paper provides a reference for subsequent research and practical application of airport slot secondary trading in the future.

Based on point merge for paired approach sequencing on closely spaced parallel runways

The paired approach is a kind of efficiency approach to closely spaced parallel runways(CSPRs),and the point merge system has the powerful interval management function,which is effective to realize the converge of traffic flows from different approach directions.In order to improve the operation efficiency of the airport terminal area,a model of paired approach sequencing based on point merge is proposed to investigate the problem of increasing the operation capacity of the closely spaced parallel runways.Taking the minimum average flight delay time as the objective,the flight distance on sequencing legs,wake turbulence separation and paired approach safety separation as constraints,the genetic algorithm is used to optimize the paired approach sequencing of arrival flights.Taking the closely parallel runways of Shanghai Hongqiao International Airport run south as an example,the point merge program is designed and the effect of model was analyzed.The results show that after optimization,the average delay time and average landing time are reduced by 40.6%and 51.8%respectively,the capacity of the closely spaced parallel runways are 1.1 times higher than the actual,the flight uptime rate can reach 100%.It is concluded that the proposed model is feasible,which can effectively reduce delay times and alleviate congestion in terminal areas.

A Brief Introduction of Air Traffic Management

Air Traffic Management (ATM) started publication in 1995 by Air Traffic Management Bureau of CAAC. It is the first magazine about ATM field in CAAC. The chairman of Editorial Board of ATM is Mr. Bao Peide, Vice Minister of CAAC. Mr. Chen Ziye, Vice President of the 1st Research Institute of CAAC and Chen Xuhua, General Director of Air Traffic Management Bureau are vice chairmen of the editorial board, and six general directors of local air traffic management bureau and some professors and experts are members of the editorial board.

A Brief Introduction of Air Traffic Management

Air Traffic Management(ATM)started publication in 1995 by Air Traffic Management Bureauof CAAC.It is the first magazine about ATM field in CAAC.The chairman of Editorial Board ofATM is Mr.Bao Peide,Vice Minister of CAAC.Mr.Chen Ziye,Vice President of the 1st ResearchInstitute of CAAC and Chen Xuhua,General Director of Air Traffic Management Bureau are vicechairmen of the editorial board,and six general directors of local air traffic management bureau andsome professors and experts are members of the editorial board.ATM includes more than 20 columns,which are communication,navigation,surveillance,air trafficmanagement,special articles(policy,planning and management of implementation of

A data-driven approach to modeling high-density terminal areas：A scenario analysis of the new Beijing,China airspace

Airports are being developed and expanded rapidly in China to accommodate and promote a growing aviation market.The future Beijing Daxing International Airport（DAX） will serve as the central airport of the JingJinJi megaregion,knitting the Beijing,Tianjin,and Hebei regions together.DAX will be a busy airport from its inception,relieving congestion and accommodating growth from Beijing Capital International Airport（PEK）,currently the second busiest airport in the world in passengers moved.We aim to model terminal airspace designs and possible conflicts in the future Beijing Multi-Airport System（MAS）.We investigate standard arrival procedures and mathematically model current and future arrival trajectories into PEK and DAX by collecting large quantities of publicly available track data from historical arrivals operating within the Beijing terminal airspace.We find that（1） trajectory models constructed from real data capture aberrations and deviations from standard arrival procedures,validating the need to incorporate data on historical trajectories with standard procedures when evaluating the airspace and（2） given all existing constraints,DAX may be restricted to using north and east arrival flows,constraining the capacity required to handle the increases in air traffic demand to Beijing.The results indicate that the terminal airspace above Beijing,and the future JingJinJi region,requires careful consideration if the full capacity benefits of the two major airports are to be realized.

Stable two-sided matching of slot allocation in airport collaborative decision making by top trading cycles mechanism

The problem of capacity shortage in some airports needs to be dealt with sustainable solutions including a more efficient use of the existing runway slots at the airports. The Collaborative Decision Making（CDM） is an important approach applied to Air Traffic Management（ATM）to achieve this efficient use of the slots allocation. Using the Matching approach for two-sided markets of Game theory, the Top Trading Cycle CDM（TTC-CDM） algorithm developed in this research is an extension of the CDM approach aggregating the Ground Delay Program（GDP）of the air sector. The paper compared the developed TTC-CDM model to the existing models such as the conventional Compression algorithm in CDM, the Trade Cycle algorithm and the Deferred Acceptance CDM（DA-CDM） model to evaluate the performance of the proposed model. Through a case study, the results show the effective application of TTC-CDM model to slot allocation in ATM and also presents the advantage of considering the preferences of airport managers beside ATC controllers and airlines in the decision processing.