Air Traffic Management and Systems IV

This chapter provides an overview of the sixth ENRI International Workshop on ATM/CNS (EIWAC2019), together with summaries of presentations in keynote sessions and special speeches. It also explains the Electronic Navigation Research Institute’s approach to organizing EIWAC. EIWAC2019 was held in Nakano, Tokyo, from October 29 to 31, 2019. In the workshop, various aspects of air traffic management (ATM) and its enablers in the fields of communication, navigation, and surveillance (CNS) were discussed.

Air traffic demand and distribution fluctuates in long-, medium-, and short-term perspective. In order to ensure safe and efficient flight operations, air navigation service providers need to ensure that enough capacity is available for airspace users. For this purpose, reliable traffic forecasts are necessary to avoid capacity shortages or excesses and subsequently costs. However, the provision of air navigation services is hampered by several effects, i.e., unpredictable traffic patterns and trends. Despite awareness of such problem, there is not a common definition or metric yet to measure the so-called ‘volatility.’ The aim of this paper is twofold: to set out an approach addressing volatility measures for different spatial and periodical scopes, and to show the effects of demand fluctuations on the ATM system from a holistic point of view.

Air transportation traffic is progressively increasing over the years and dealing with it is an essential task to guarantee fluid flights in the future. Several works already indexed multiple aspects of aviation, among them, the E-MAN system. It introduced the sequencing of arriving traffic, starting from early stages of the En-route phase. This change facilitated the work for the approach controllers but also increased the workload of the En-route controllers. To handle that workload, controllers are now assisted by tools that consider the new constraints introduced by the arrival management system and propose advisories. From that same perspective, our project focuses on an algorithm for a helper tool that will combine both aspects of traffic sequencing in the En-route phase and conflict resolution. With this novel approach, we automatically generate near-to- optimal flight decisions, given that we can modify the speed and the flight level to respect the sequencing constraints and cut down potential conflicts. We categorize the problem as a mathematical optimization case. Thus, we describe a detailed mathematical model which covers all the aspects of the problem. This model gives a basis for the implementation of the flight optimizer. Later, we propose a solution based on a sliding window simulated annealing algorithm which reduces the complexity and takes into account uncertainties. Finally, we successfully test an implementation of the solution with real-life traffic data. It corresponds to flights within France going towards Paris CDG airport over a period of 24 h. The results demonstrate a total conflicts resolution with satisfying compliance with sequencing constraints.

Accommodating the air traffic growth, reducing arrival delay is one of the most important functions of designing the ATM system. One of the newest concepts to further optimize arrival flows is multi-stage arrival management, proposed by DLR, in which different guidance principles to manage the arriving traffic are implemented in different stages. These stages are optimized to the core management task to be done in a certain area of the arrival stream and the conditions of the surrounding environment. This paper discusses this concept through a macroscopic analysis on the overall arrival traffic flows. Further, this paper analyzes parts of the multi-stage arrival management concept applied to Tokyo International Airport as a case study. A stochastic characteristic of arrival trajectories will be discussed as a counterpart of conventional deterministic trajectory-based operation based on data-driven analysis and arrival procedures at the airport. The best strategies of shifting arrival flow control to time-based management are analyzed based on the stochastic data analysis. Impacts of pop-up aircraft are discussed as one of the causes to increase uncertainties in aircraft trajectory management.

Analysis of punctuality of airport arrivals, as well as identification of causes of the delays within transition airspace, is an important step in evaluating performance of the Terminal Maneuvering Area (TMA) Air Navigation Services. In this work we analyse how different weather events influence arrival punctuality and vertical flight efficiency on example of Stockholm Arlanda airport. We quantify the impact of the deviations from the flight plans influenced by different weather events, by demonstrating that they result in significant arrival delays, vertical inefficiencies and calculating how much extra fuel is wasted due to vertical flight inefficiency within Stockholm TMA.

Increasing air traffic creates many challenges for air traffic management (ATM). A general answer to these challenges is to increase automation. However, communication between air traffic controllers (ATCos) and pilots is still widely analog and far away from digital ATM components. As communication content is important for the ATM system, commands are still entered manually by ATCos to enable the ATM system to take the content of the communication into account. However, the disadvantage of this procedure is significant additional workload for the ATCos. To avoid this additional effort, automatic speech recognition (ASR) can automatically analyze the communication and extract the content of spoken commands. DLR together with Saarland University invented the AcListant® system, the first assistant based speech recognition (ABSR) with both a high command recognition rate and a low command recognition error rate. Beside the high recognition performance, AcListant® project revealed shortcomings with respect to costly adaptations of the speech recognizer to different air traffic control (ATC) environments. Machine learning algorithms for the automatic adaptation of ABSR to different airports were developed to counteract this disadvantage within the MALORCA project, funded by Single European Sky ATM Research Programme 2020 Exploratory Research (SESAR-ER). To support the standardization of speech recognition in ATM, an ontology for ATC command recognition on semantic level was developed to enable the reuse of expensively manually transcribed ATC communication in the SESAR Industrial Research project PJ.16-04. Finally, results and experiences are used in two further SESAR Wave-2 projects. For the first time, this paper presents the evolution from the idea of ABSR born in an academic environment, starting with the project AcListant®, to industrialization ready research prototype of technology reediness level (TRL) 4. In this course, relevant industrial needs such as costs and necessary standardizations supported by tailored European funding scheme are considered. The addressed SESAR projects are MALORCA, PJ.16-04, PJ.10-96 HMI Interaction modes for ATC centre, and PJ.05-97 HMI Interaction modes for Airport Tower.

The ground movement is one of the most critical airside operations. It includes two sub-problems: routing and scheduling and serves the purpose of guiding aircraft on the surface of an airport to meet the departure schedule while minimizing overall travel time. To achieve that purpose, ground movement controllers manage the taxi-route assignments and taxi-time estimation for each aircraft in arrival or departure queue. A high-accuracy taxi-time calculation is required to increase the efficiency of airport operations. In this study, we propose a data-driven approach to construct features set and build predictive models for taxi-time prediction for departure flights. The proposed approach can suggest the taxi-route and predict the corresponding taxi-time by analyzing ground movement data. The controller’s operational preferences are extracted and learned by machine learning algorithms for predicting taxi-route and taxi-time of given aircraft. In this approach, we take advantage of taxiing trajectories to learn the controller’s decision, which reflects how the controller had decided the routing for a given situation. Two machine learning models, random forest regression, and linear regression are implemented and show similar performances in estimating the taxi-time. However, since the random forest is an ensemble method that has advantages in handling outliers, performing feature selection, and assessing feature importance, it can provide more stable results and interpretability, for real operations. The predictive model for taxi-time can predict the taxi-out time with high accuracy with given assigned taxi-route. The model can cover the controller’s decision up to 70% in the top-1 and 89% in top-2 recommends. The mean absolute error is less than 2.07 min for all departure flights, and root mean square error is approximately 2.5 min. Moreover, the ± 3-minute error window can cover around 76% of departures, while more than 95% of departures are within the ± 5-minute error window.

This paper will provide an insight into enhanced collaborative decision making being conducted in adverse weather conditions in a simulated Oslo airport environment. This simulation is part of the Total Airport Management (TAM) research in the Single European Sky ATM Research program (SESAR 2020) as project PJ.04. SESAR2020 is operated in two defined program waves, wave 1 covering the years 2016–2019 and wave 2 following until 2022. This paper will focus on a set of two out of seven V2 level validation exercises that are conducted in wave 1 of PJ.04′s Solution 2 (PJ.04–02), addressing concepts for Collaborative Airport Performance Management. The key objective of PJ.04–02 is to develop multi-stakeholder decision support for airport management stakeholders especially in adverse conditions (e.g. bad weather, union strikes or unforeseen events such as runway blockages). In the Oslo airport environment winter conditions are a major reason for performance degradation of airport operations. An enhanced integration of stakeholder actions and collaborative operations planning is expected to provide performance benefits. The philosophy of collaborative decision making advocated by SESAR 2020 is that of ‘consensus’ building amongst the different airport stakeholders through a common impact assessment and a structured solution finding process resulting in mutually agreed actions. The objective is to raise situation awareness and introduce a collaborative problem solving approach leading to better, earlier and therefore more stable solutions. Orchestrated by a moderator, the so-called airport operations center supervisor, the global impact assessment is supported by each stakeholder evaluating the consequences for their own operations, documented in an electronic impact and corresponding solution message. The validation and assessment of the underlying conceptual approach to collaborative airport performance management in adverse conditions requests for an artificial airport environment. In contrast to a real airport the simulated environment allows for any necessary changes in weather situation, traffic patterns or support system composition while keeping the operators in the loop. The target level for the validation experiments is V2 (referring to the European operational concept validation methodology), requiring the simulator to allow for an operational concept feasibility assessment while providing emulation of all airport processes and working positions of the airport management. The objective assessment of benefits credited to specific operational improvements under consideration of the validation exercises PJ.04–02.V2.04 and PJ.04–02.V2.09 requires a stepwise approach in which the functionality and system complexity is consecutively enhanced. The baseline was represented by the results achieved by SESAR 1, providing an airport operations center (APOC), basic processes and support system functionality. The functionality of the V2.04 setup reflected the SESAR2020 solution regarding advanced decision support, providing dynamic demand and capacity balancing alongside a guided enhanced collaborative decision making process and enhanced meteorological forecasts by weather alerts. The V2.09 solution setup provides additional information support by a performance dashboard, taking into account probabilities for additional diverted traffic. Two exercise simulation runs were executed for each setup, subjecting the Oslo operators with different meteorological phenomena and resulting operational challenges. The PJ.04–02 validation objectives were broken down into exercise specific objectives, allowing for an impartial feasibility assessment based on objective metrics and qualitative human performance criteria. Preliminary exercise analysis results indicate a well-received conceptual approach, the stepwise functionality enhancement complying with benefit increase expectations.

The European funded project “GNSS Solutions for Increased GA and Rotorcraft Airport Accessibility Demonstration—GRADE” is a very large demonstration (VLD) project within the SESAR2020 program. In the context of this VLD, several SESAR solutions are demonstrated in simulation and real flight test campaigns. This paper presents the demonstration and evaluation of capabilities of a common used general aviation (GA) aircraft, namely Cessna 172N, performing different precision approach procedures. In parallel, an EC135 helicopter is demonstrating the possibilities of point in space (PINs) approaches. The project team consists of European partners representing different stakeholders involved in air traffic. Initially, fast time simulations took place where air traffic controllers were confronted with aircraft approaching on curved approaches paths using performance-based navigation (PBN) procedures. A flight test campaign has been conducted in July and September 2019 in Braunschweig. The two project partners TU Braunschweig (TUBS) and German Aerospace Centre (DLR) conducted several approaches in parallel using a GA aircraft (TUBS) and a research helicopter (DLR) in order to demonstrate a simultaneous non-interfering (SNI) approach to the Braunschweig airport.

The Aircraft Landing Problem consists in sequencing aircraft on the available runways and scheduling their landing times taking into consideration several operational constraints, in order to increase the runway capacity and/or to reduce delays. In this work we propose a new Mixed Integer Programming (MIP) model for sequencing and scheduling aircraft landings on a single or multiple independent runways incorporating safety constraints by means of separation between aircraft at runways threshold. Due to the NP-hardness of the problem, solving directly the MIP model for large realistic instances yields redhibitory computation times. Therefore, we introduce a novel heuristic search methodology based on Optimistic Planning that significantly improve the FCFS (First-Come First-Served) solution, and provides good-quality solutions in reasonable computational time. The solution approach is then tested on medium and large realistic instances generated from real-world traffic on Paris-Orly airport to show the benefit of our approach.

This paper aims at investigating further the use of the social media Twitter as a real-time estimator of the US Air Transportation system. Two different machine learning regressors have been trained on this 2017 passenger-centric dataset and tested on the first two months of 2018 for the estimation of air traffic delays at departure and arrival at 34 different US airports. Using three different levels of content-related features created from the flow of social media posts led to the extraction of useful information about the current state of the air traffic system. The resulting methods yield higher estimation performances than traditional state-of-the-art and off-the-shelf time-series forecasting techniques performed on flight-centric data for more than 28 airports. Moreover the features extracted can also be used to start a passenger-centric analysis of the Air Transportation system. This paper is the continuation of previous works focusing on estimating air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study suggest a method to use passenger-centric data-sources as an estimator of the current state of the different actors of the air transportation system in real-time.

Continuous descent operations (CDO) is an efficient aircraft descent procedure that results in minimal fuel consumption because aircraft descend from their optimal top of descent (TOD) at idle engine thrust. To expand the implementation of CDO, we focus on enhancing the decision-making abilities of air traffic controllers (ATCOs). We conducted a series of human-in-the-loop (HITL) simulations to understand the issues involved in CDO approval decision making by ATCOs and to provide effective inputs to support the decision making. From our initial simulation results, we identified several issues that can affect ATCO CDO-specific decisions. As a proposal to solve these issues, we then created support information displays and evaluated them in follow-on simulations. Our support displays were found to be increasingly effective if their information was sufficiently accurate to avoid premature judgment. It was also found necessary to provide support information to ATCOs to enable more proactive air traffic control (ATC) measures for CDO execution.

Aircraft approach flight path safety management provides procedures that guide the aircraft to intercept the final approach axis and runway slope before landing. In order to detect atypical behavior, this paper explores the use of data generative models to learn real approach flight path probability distributions and identify flights that do not follow these distributions. Through the use of Generative Adversarial Networks (GAN), a GAN is first trained to learn real flight paths, generating new flights from learned distributions. Experiments show that the new generated flights follow realistic patterns. Unlike trajectories generated by physical models, the proposed technique, only based on past flight data, is able to account for external factors such as Air Traffic Control (ATC) orders, pilot behavior or meteorological phenomena. Next, the trained GAN is used to identify abnormal trajectories and compare the results with a clustering technique combined with a functional principal component analysis. The results show that reported non compliant trajectories are relevant.

Convective weather and its inherent uncertainty constitute one of the major challenges in the air traffic management (ATM) system, entailing both safety hazards and economic losses. In the present work, we propose a stochastic algorithm for trajectory planning that ensures feasibility and safety of the path between two points while avoiding unsafe stormy regions. The uncertain zone to be flown is described by an ensemble of equally likely forecasts. We design a scenario-based optimal rapidly exploring random tree (SB-RRT*), and we able to dynamically allocate risk during its expansion so that a safety margin is not violated. The solution is a safe continuous trajectory that minimizes the distance covered. We present preliminary results assuming weather to be the only source of uncertainty. We consider an aircraft point-mass model at constant altitude and airspeed with manoeuvres being limited by a minimum turning radius.

The future Air Traffic Management (ATM) system will depend on Trajectory Based Operations (TBO) to accommodate the growing demand in air traffic. This system will expect aircraft to follow an assigned 4D-trajectory with high precision, meeting arrival times over established checkpoints with great accuracy. These time-constraints are called Target Windows (TWs). Wind is one of the greatest sources of uncertainty and, consequently, a key point for the improvement of predictability and, ultimately, the implementation of 4D-trajectories. The main aim of this paper is to develop a methodology to characterize these TWs and to assess the uncertainty on the evolution of 4D-trajectories due to the effect of wind. For such purpose, 4D-trajectories are modelled deterministically, using a point mass model and the BADA (Base of Aircraft Data) methodology of EUROCONTROL. In parallel, wind is modelled with a hybrid approach, where the stochastic component captures the error associated with weather forecasts. Through Monte Carlo Simulation, the variability of the trajectory´s parameters is evaluated under different atmospheric scenarios. Using these results, TWs are defined along the different stages of flight, quantifying the uncertainty associated with the aircraft´s position under the effect of wind.

Monitoring performance is an essential part of an air traffic management system and requires appropriate metrics such as complexity and safety corresponding to the monitoring objectives. We have proposed as a new metric an air traffic control (ATC) difficulty index that quantifies the 'difficulty' of an air traffic situation from an air traffic controller workload perspective. Once a traffic situation with a potentially high difficulty is predicted, it is desirable that an advisory should be provided to controllers to enable the trajectory modification of key aircraft to mitigate the difficulty. To investigate the feasibility of developing a controller decision support tool that can provide candidate trajectory modifications to mitigate high difficulty traffic situations, we have started a conceptual study based on simulations. This paper reports preliminary results of the study. We first describe the background of the study and briefly explain the proposed difficulty index. Then, we indicate the concept and algorithm for automatic trajectory modification and describe our approach for moving forward and challenges.

The current ground-based collaboration environment is not sufficient to enable the full range of benefits defined in the ICAO Global Air Navigation Plan (GANP). In order to achieve a safe, secure, high-performing, and sustainable global air traffic management, the collaborative information exchange should be achieved for not only ground operational systems but also connected aircrafts. However, it is difficult for the current command-and-control Air-to-Ground (A/G) communication approaches to satisfy different and extensive information exchanges between the aircraft and the air navigation service providers. To promote the implementation of information collaborative environment in pre-departure phase and improve operational awareness and Collaborative Decision Making (CDM) through information exchange, the Electronic Navigation Research Institute (ENRI) has developed a test system. Several practical experiments have been deployed based on the integration of these systems to show the benefit of A/G SWIM (System-Wide Information Management) integration. In this paper, the concept and the technical framework of A/G SWIM integration are introduced. To provide timely, relevant, accurate, authorized, and quality-assured information for high-assurance operation, the multi-layered system architecture and the collaborative information exchange technology are proposed. Moreover, the development of practical validation system for ground taxiing experiment is presented. Finally, the definition and comparison of communication quality, information quality, and service quality for constructing the collaborative operating environment to include interactions of A/G stakeholders, systems, and services through the A/G SWIM integration are discussed.

This contribution discusses shadowing effects of large objects with respect to navigation and communication systems. Since the current practice for assessing the impact of scattering objects on those systems is based on mainly quasi-optical wave propagation, a simplified, but yet improved analysis of possible disturbances is proposed. Based on scattering theory, a simple analytic formula is derived applying fundamentals of the radar cross section concept. As for at least canonical objects, such as metal plates or cylinders, for which an accurate analytic description for the radar cross section is well known, simple shadowing examples are discussed. With the derived formula a more sophisticated sensitive area layout can be developed than it is currently recommended with an overall layout, i.e. typically a circular area with a fixed radius. Finally, measurement results in a miniaturized anechoic chamber are presented that give additional insight into the shadowing phenomena discussed in this contribution. Further, measurement results are shown as examples for travelling waves and shadowing effects.

This paper describes an approach to the ADS-B coverage design being undertaken for the mountainous terrains of Bhutan. Existing ADS-B implementation studies have mostly focused on coverage design based on interference criteria. There is a lack of ADS-B coverage design studies in challenging terrain like in the Himalayan kingdom, where about 98% of the land cover is mountains. To account for the unique environment, a physical optics-based deterministic channel modeling methodology is adopted. A radio siting algorithm developed to determine the best location of additional ADS-B receivers is outlined. The effectiveness of the algorithm is demonstrated by applying it to determine the location of additional ADS-B receiver at PARO control zone to improve coverage in areas critical to flight operations. This study will be augmented by analysis of opportunistic ADS-B signal measurements being carried out before the ADS-B receiver network is implemented for use in air traffic management purpose.

Regular flight inspection of navigation systems, such as instrument landing system (ILS) and the VHF omnidirectional radio range (VOR), is an important part of maintenance to ensure safe flight operations. Usually, this is done with aircraft flying and measuring at trajectories according to international recommendations from the ICAO. With the arising technology of unmanned aerial vehicles (UAVs), such flight inspections could be performed in a much more economic and efficient manner, in particular, if combined with nearfield measurement technology, allowing farfield predictions anywhere in space without limitations to particular flight trajectories. This contribution discusses an innovative approach for such nearfield inspections with UAV as research focus of the navigation aid antenna characterization—next generation (NAVANT-NG) project with corresponding requirements and first measurement results. In this contribution, both the dynamic range and the accuracy of phase measurements and stability are investigated. In particular, the accuracy and stability of phase measurements are a crucial part in such nearfield measurements. First measurement results with a test antenna and a UAV demonstrate the feasibility of proposed nearfield measurements.