Due to projectedincrea ses in air traffic, there are several research efforts underway to evaluate ways to safely increase the capacity of the National Airspace System (NAS), improve operational efficiency, andre duce aircraft noise. At NASA Langley Research Center (LaRC) in Hampton, Virginia, two parallel research efforts have focusedon terminal area research: one is Airborne Precision Spacing (APS), and the other is the Quiet Aircraft Technologies (QAT) project. The APS objective is to increase terminal-area capacity without adversely affecting safety, whereas the QAT project objective is to develop noise- and fuel-efficient approach trajectories. The APS project developed a cockpit tool, called Airborne Merging and Spacing for Terminal Arrivals (AMSTAR), that issues speedco mmands to aircraft to maintain desired spacing between aircraft pairs. The APS studies showed an ability to increase runway capacity; however, capacity increases may negatively impact noise andemissio n levels in airport areas. The QAT project created efficient Continuous Descent Approaches (CDAs), which showedred uctions in aircraft ground noise and fuel consumption. Previous research has shown that CDA trajectories have adverse effects on runway capacity because aircraft must be spacedf urther apart at long distances from the runway to prevent separation losses at the runway threshold. To date, the APS and CDA concepts have been evaluated independently at LaRC.In this study, three different approaches to combining APS and CDA operations were evaluatedto determine the feasibility and benefits of combining these concepts. These methods combined AMSTAR with 3◦-flight-path-angle-CDA approach routes, 3◦-CDA routes with spoilers, and2 ◦-CDA routes without spoilers. Adding the use of spoilers allowedf aster responses to large speedr eductions issued by AMSTAR. This improvement was contrastedwith the effects of a shallower flight-path angle for greater deceleration capabilities. This research indicated that AMSTAR improved the performance of CDA operations, although full capacity improvements were not achieved. Whereas the 2◦-CDA routes were expectedto show the best results, the 3◦-CDA case with spoilers showed the least variability in thresholdspacing errors. All of the CDA routes were more noise, fuel, and time efficient than traditional step-descent routes that are commonly usedto day.

Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Increasing air traffic and urbanisation has led to a cluttered airspace, particularly near airports, where both complex terrain and multiple moving obstacles are frequent. Accurately and efficiently predicting violations in safe separation criteria for commercial aircraft, a process called conflict detection, is therefore crucial in assessing risk associated with threats of collision. Existing avoidance systems in operation such as TCAS, EGPWS and ATC exhibit shortcomings, leaving room for uncertainty and possible conflict scenarios. A single on-board system capable of minimising errors in prediction would inform conflict resolution decisions more accurately as well as support the notion of free flight, an objective of next-generation air traffic management systems. This thesis investigates the viability of a modern algorithm, probability flow, as a method of probabilistic conflict detection for commercial aircraft in airport environments. Simulation results for realistic flight scenarios are presented in comparison with a ground-truth result obtained through Monte Carlo simulation. Observations are made regarding the suitability of probability flow for real-world application. It is found that probability flow is capable of calculating a tight upper bound to the probability of conflict quickly and accurately for most conflict scenarios. However, unreasonably large overestimates on the probability of conflict are obtained when flying parallel to an obstacle conflict region. This problem could lead to a high frequency of false alerts, particularly in aborted landing scenarios and at airports operating parallel runways. It is therefore advised that further research be conducted to resolve this problem before probability flow can be reliably implemented in an airport environment. / AFRIKAANSE OPSOMMING: Toenemende lugverkeer en verstedeliking het gelei tot ‘n deurmekaar lugruim, veral naby lughawens, waar beide komplekse terrein en verskeie bewegende struikelblokke gereeld voorkom. Akkuraat en doeltreffende voorspelling van oortredings in veilige skeidingskriteria vir kommersiële vliegtuie, naamlik konflik opsporing, is dus van kardinale belang in die beoordeling van die risiko wat verband hou met dreigemente van ‘n botsing. Bestaande vermyding stelsels in werking soos TCAS, EGPWS en ATC toon tekortkominge, wat ruimte laat vir onsekerheid en moontlike konflik scenario’s. ‘n Enkele aanboordstelsel, wat in staat is om foute in voorspelling te verminder, sou konflikresolusie besluite meer akkuraat in kennis stel, asook om die idee van vrye vlug te ondersteun, ‘n doelwit van toekomstige lugverkeer beheerstelsels. Hierdie tesis ondersoek die lewensvatbaarheid van ‘n moderne algoritme, waarskynlikheidsvloei, as ‘n metode van probabilistiese konflik opsporing vir kommersiële vliegtuie in die lughawens omgewing. Simulasie resultate vir realistiese vlug scenario’s word aangebied in vergelyking met ‘n grond-waarheid resultaat wat verkry word deur middel van Monte Carlo simulasie. Waarnemings word gemaak ten opsigte van die geskiktheid van waarskynlikheidsvloei vir die werklikheid. Dit is bevind dat waarskynlikheidsvloei in staat is om die berekening van ‘n stywe bogrens tot die waarskynlikheid van konflik vinnig en akkuraat te bepaal vir die meeste konflik scenario’s. Tog is daar ‘n onredelike groot oorskatting op die waarskynlikheid van konflik wat verkry word wanneer ‘n vliegtuig parallel met ‘n hindernis konflik streek vlieg. Hierdie probleem kan lei tot ‘n hoë frekwensie van valse waarskuwings, veral in mislukte landing scenario’s en by lughawens wat van parallel aanloopbane gebruik maak. Dit word dus aanbeveel dat verdere navorsing gedoen word om die probleem op te los voordat waarskynlikheidsvloei betroubaar in ’n lughawe omgewing geïmplementeer word.

The scope of this study was to evaluate the impact of the air traffic controller-to-pilot communication standard known as CPDLC or Data-Communication on the future air traffic operations. The impact was evaluated from the double viewpoint of airport delays and air traffic controllers' workload. RAMS simulation software is used to perform all the runs and from its output data the values of terminal area delays and controllers workload are obtained. The New York Metroplex terminal area was used as a case study. Because of its complexity, where three major airports (i.e. JFK, Newark, and La Guardia) interact and constraint each other, this area was particularly interesting to be studied and the data analyzed gave a valuable insight on the possible future impact of Data-Communication in congested terminal areas. The results of the study, based on some previous man-in-the-loop simulations performed by the FAA in the nineties, showed that significant potential benefits could be obtained with the complete implementation of such technologies in the workload experienced by air traffic controllers. Moreover some small but not negligible benefits were obtained in the total delays accrued by each airport studied. On the other hand, the simulations of the future demand predicted by the FAA demonstrated that without a significant increment in capacity or limitation on the traffic growth intolerable delays would be recorded across the NAS in the future. For the complexity of the simulation model calibration and for the very time-consuming run time not all the scenarios described in the methodology were tested, demonstrating the weakness of RAMS as a ground simulation model. / Master of Science

This thesis addresses the possibility of using a delay-on-ground concept in which flights with less than 1 hour flying time (often referred to as pop-up flights) absorb their arrival sequencing delay at the departure gate by being issued their Arrival Manager (AMAN)-scheduled time as a Required Time of Arrival (RTA) that is inserted into the Flight Management System (FMS). Due to their short duration these flights are currently often inserted into the AMAN sequence shortly before Terminal Manoeuvring Area (TMA) entry and thereby often need to absorb their arrival sequencing delay in the inefficient manner of airborne holding or vectoring close to the arrival airport. The literature review examines current operational procedures of AMANs and Departure Managers (DMANs), the current FMS RTA function and live trials in which the delay-on-ground concept was tested in real operations. A case study airport in Europe that has potential to benefit from the concept is identified. The performance of the delay-on-ground concept for the case study airport is then assessed by performing 180 fast-time Monte Carlo simulation runs. For each run the arrival flow to the case study airport and the departure flows from two medium-sized airports from which the pop-up flights originate are simulated. Each run represents an operational day and variations in departure/arrivals time is put into the timetables to simulate the variation in actual departure/arrival times resulting from operational factors normally encountered in day-to-day operations. An algorithm is written in Matlab to simulate an AMAN-DMAN cooperation in which pop-up flights are locked to the required departure times to meet their RTAs. It is shown that a significant reduction in airborne delay time and fuel consumption can be achieved at the case study airport by using the concept. It is also shown that it is possible to ensure that the pop-up flights depart at the required times to meet their RTAs without negatively affecting the departure sequences.

<p>This report describes how an aircraft creates and flies its User Preferred Trajectory from take-off to landing, based on the objectives and constraints the aircraft is subjected to from a technological and operational viewpoint.</p><p>A basic description of commercial aircraft operation is given, with an emphasis on identifying the different stakeholders (Air Navigation Service Providers, Airline Operation Center, Pilot/Aircraft, Airport and Civil Aviation Authority). A general description of Instrument Flight Rules operations is also given, together with an explanation of the capabilities of modern flight management systems.</p><p>The objectives and constraints of the trajectory building process from an aircraft and air traffic management viewpoint are described in Chapter 4. Those are instrumental in understanding how the user preferred trajectory is built. The initial and detail route planning process is then described.</p><p>The initial route planning is performed long before the flight and usually by the airline operating center, while detail flight planning, including take-off, runway and departure procedure is performed later by the crew. This process is re-performed minutes before take-off, and usually iterated during the flight when the details of approach and landing are communicated to the aircraft crew.</p><p>The implementation of this user preferred trajectory is explained in terms of the options that the pilots have in the aircraft avionics to perform the mission. The implementation explained in this report is based on the avionics suite of a Boeing 737NG aircraft equipped with the most advanced flight management systems.</p><p>An implementation of a user preferred trajectory, where the aircraft crew is able to best fulfill their objectives is composed of an idle or near idle descent from the cruise altitude. This type of descent, called an advanced continuous descent approach has been implemented by some air navigation service providers, airlines and airports, based on advanced technology that will be further described in this paper. Those procedures are called Green Approaches.</p><p>In the last part of this report, the benefits of flying Green Approach procedures are analyzed by means of aircraft simulations. The analysis describes in detail the lateral and vertical trajectories of the Green Approaches at Stockholm’s Arlanda Airport and Brisbane Airport (Australia), together with the calculated advantages in term of fuel consumption, noise and gas emissions.</p>

Thesis analyses present status of air traffic management in European area from historical and legislative point of view. Subjects of thesis are present projects in ATM and future variants of organization of air traffic in EU. Practical part focuses on impact of ATM on air transportation.

<div>The air traffic system is one of the most complex and safety-critical systems, which is expected to grow at an average rate of 0.9% a year -- from 51.8 million operational activities in 2018 to 62 million in 2039 -- within the National Airspace System. In such systems, it is important to identify degradations in system performance, especially in terms of safety and efficiency. Among the operations of various subsystems of the air traffic system, the arrival and departure operations in the terminal airspace require more attention because of its higher impact (about 75% incidents) on the entire system's safety, ranging from single aircraft incidents to multi-airport congestion incidents.</div><div><br></div><div>The first goal of this dissertation is to identify the air traffic system's degradations -- called anomalies -- in the multi-airport terminal airspace or metroplex airspace, by developing anomaly detection models that can separate anomalous flights from normal ones. Within the metroplex airspace, airport operational parameters such as runway configuration and coordination between proximal airports are a major driving factor in aircraft’s behaviors. As a substantial amount of data is continually recording such behaviors through sensing technologies and data collection capabilities, modern machine learning techniques provide powerful tools for the identification of anomalous flights in the metroplex airspace. The proposed algorithm ingests heterogeneous data, comprising the surveillance dataset, which represents an aircraft’s physical behaviors, and the airport operations dataset, which reflects operational procedures at airports. Typically, such aviation data is unlabeled, and thus the proposed algorithm is developed based on hierarchical unsupervised learning approaches for anomaly detection. This base algorithm has been extended to an anomaly monitoring algorithm that uses the developed anomaly detection models to detect anomalous flights within real-time streaming data.</div><div><br></div><div>A natural next-step after detecting anomalies is to determine the causes for these anomalies. This involves identifying the occurrence of precursors, which are triggers or conditions that precede an anomaly and have some operational correlation to the occurrence of the anomaly. A precursor detection algorithm is developed which learns the causes for the detected anomalies using supervised learning approaches. If detected, the precursor could be used to trigger actions to avoid the anomaly from ever occurring.</div><div><br></div><div>All proposed algorithms are demonstrated with real air traffic surveillance and operations datasets, comprising of departure and arrival operations at LaGuardia Airport, John F. Kennedy International Airport, and Newark Liberty International Airport, thereby detecting and predicting anomalies for all airborne operations in the terminal airspace within the New York metroplex. Critical insight regarding air traffic management is gained from visualizations and analysis of the results of these extensive tests, which show that the proposed algorithms have a potential to be used as decision-support tools that can aid pilots and air traffic controllers to mitigate anomalies from ever occurring, thus improving the safety and efficiency of metroplex airspace operations.</div>

The procedure of testing the implemented software is important and should be an essential and integrated part of the development process. In order for the testing to be meaningful it is important that the testing procedure ensures that the developed software meet certain requirements. The testing procure is often controlled by some sort of test specification. For many companies it is desirable to automate this procure. The focus of this thesis has been to automate a small subpart of the manual tests today performed related to SAAB:s air traffic management system. The automation has been achieved by studying the existing test specification which involves a lot of manual operations and to write software that mimics a few of these test cases. The thesis has resulted in a test framework which automates a small subset of the manual tests performed today. The framework has been designed to be scalable and to easily allow more test cases to be added by the personnel when time permits. The test framework has also been integrated with SAAB:s existing CI/CD workflow.

<div>With the growing demand of air traffic, it becomes more important and critical than ever to develop advanced techniques to control and monitor air traffic in terms of safety and efficiency. Especially, trajectory prediction can play a significant role on the improvement of the safety and efficiency because predicted trajectory information is used for air traffic management such as conflict detection and resolution, sequencing and scheduling. </div><div><div>In this work, we propose a new framework by integrating</div><div>the two methods, called hybrid data-driven and physics-based trajectory prediction. The proposed algorithm is applied to real air traffic surveillance data to demonstrate its performance.</div></div>

<div> <div> <div> <p>As the demand and complexity of air traffic increase, it becomes crucial to maintain the safety and efficiency of the operations in airspaces, which, however, could lead to an increased workload for Air Traffic Controllers (ATCs) and delays in their decision-making processes. Although terminal airspaces are highly structured with the flight procedures such as standard terminal arrival routes and standard instrument departures, the aircraft are frequently instructed to deviate from such procedures by ATCs to accommodate given traffic situations, e.g., maintaining the separation from neighboring aircraft or taking shortcuts to meet scheduling requirements. Such deviation, called vectoring, could even increase the delays and workload of ATCs. This thesis focuses on developing a framework for trajectory pattern identification and classification that can provide ATCs, in vectored airspace, with real-time information of which possible vectoring pattern a new incoming aircraft could take so that such delays and workload could be reduced. This thesis consists of two parts, trajectory pattern identification and trajectory pattern classification. </p> <p>In the first part, a framework for trajectory pattern identification is proposed based on agglomerative hierarchical clustering, with dynamic time warping and squared Euclidean distance as the dissimilarity measure between trajectories. Binary trees with fixes that are provided in the aeronautical information publication data are proposed in order to catego- rize the trajectory patterns. In the second part, multiple recurrent neural network based binary classification models are trained and utilized at the nodes of the binary trees to compute the possible fixes an incoming aircraft could take. The trajectory pattern identifi- cation framework and the classification models are illustrated with the automatic dependent surveillance-broadcast data that were recorded between January and December 2019 in In- cheon international airport, South Korea . </p> </div> </div> </div>