<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>

<p>Due to the rapid development of technologies for unmanned aircraft systems (UAS's), the supply and demand market for UAS's is expanding globally. With the great number of UAS's ready to fly in civilian airspace, an UAS aircraft traffic management system that can guarantee the safe, resilient and efficient operation of UAS's is absent. The vast majority of existing literature on UAS traffic lacks of the attention to the fundamental characteristics of UAS operation, which leads to models and methods that are difficult to implement or lacks scalability. Motivated by these challenges, this research aims at achieving three objectives: 1) the proper frameworks that scale well with high-frequency, high-density UAS operations, 2) the models that captures the fundamental characteristics of UAS operations, 3) the methods that can be implemented in practice with guarantees of efficiency, safety, and resilience. In particular, the objectives are studied at low-level UAS traffic congestion control, agent-level UAS configuration control and unknown agent prediction. The proposed frameworks and obtained results offer comprehensive and practical guidelines of real world UAS operations at different levels.</p>

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The purpose of this report is to improve the flow of trafficin intersections through the use of a dynamic algorithm.People spend on average more than six weeks commuting towork in Stockholm every year. A large part of the time thatis spent in traffic is due to the time delay in intersections.In this report, sensors that measure speed and distanceto the vehicle are used instead of detectors that only knowif a car is there or not. There are existing solutions that canoptimise the flow for autonomous cars but since the trafficmay consist of a mix of autonomous and human drivenvehicles during the upcoming 40 years those solutions arenot usable for some time.In this work, a system that can handle both autonomousand human driven vehicles is created. The limitation of thesystem is that it can only receive two cars coming from differentdirections simultaneously. The system does not workfor car queues. The system measures the speed of- and thedistance to the vehicles continuously.According to the simulations that were made the algorithmthat has been designed through this project is moretime efficient than the system that is in place today, assumingthat the assumptions that were made for the currentsystem are correct.The conclusion in this report is that it is possible tomake a system that is more time efficient than the one thatis in use today. / Syftet med den här rapporten är att förbättra flödet ikorsningar genom en dynamisk algoritm. Människor sitterdrygt 6 veckor i bilköer varje år. En stor del av av denspenderande tiden i traffiken är på grund av att fordonbehöver stanna i korsningar.I den här rapporten har sensorer som mäter hastighetoch distans använts istället för dagens detektorer som endastkänner av om ett fordon kör över detektorn eller inte.Det finns andra rapporter med lösningar för att öka flödeti korsningar för självkörande bilar men om man kollar pådet kommande 40 åren kommer det troligtsvis att vara enblandning av självkörande och mänskligt körda bilar.I det här arbetet skapas ett system som kan interageramed både mänskligt körda och autonoma bilar. Begränsningarnai det här systemet är att systemet endast kan taemot två bilar som kommer från olika ingångar i korsningensamtidigt. Systemet fungerar inte för bilköer. Systemet mäterden nuvarande hastigheten och distansen på fordonen.Systemet fungerar för alla olika kombinationer av mänskligtoch självkörande bilar.Resultatet av den här rapporten är att en algoritm harutvecklats och är mer tidseffektivt än systemet som användsi Sverige idag, med våra antaganden om systemet som harutveklats i den här rapporten och systemet som användsidag. Resultatet är baserat på korsningar där bara två bilarmöts utan köer.Slutsatsen av den här rapporten är att det är möjligtatt göra ett system som är mer tidseffektivt än systemetvi använder oss av idag, men vi kan inte säkertsätlla attsystemet i den här rapporten är mer robus och driftsäkertän det som används i Sverige idag.

A modeling framework to evaluate the costs and benefits of implementation of Controller Pilot Data Link Communication (CPDLC), and Air Traffic Management (ATM) decision support tools is proposed in this paper. The benefit/cost evaluation is carried out for four key alternatives namely alternative A: Do nothing scenario (only voice channel), alternative B: Voice channel supplemented with CPDLC, alternative C: Alternative B with ATM tools in a non-integrated scenario and finally alternative D: Alternative B with ATM tools in an integrated scenario. It is a bi-level model that captures the linkages between various technologies at a lower microscopic level using a daily microscopic model (DATSIM) and transfers the measures of effectives to a higher macroscopic level. DATSIM stands for Data Link and Air Traffic Technologies SIMulation and it simulates air traffic in the enroute sector and terminal airspace for a single day and captures the measures of effectiveness at a microscopic level and feeds its output to the macroscopic annual model which then runs over the entire life cycle of the system. Airspace dwell time benefit data from the microscopic model is regressed into three dimensional benefit surfaces as a function of the equipage level of aircraft and aircraft density and embedded into the macroscopic model. The main function of the annual model is to ascertain economic viability of any deployment schedule or alternative over the entire life cycle of the system. The life cycle cost model is composed of four modules namely: Operational benefits module, Safety benefit module,Technology cost module and Training cost module. Analysis using the model showed that an enroute sector gets congested at aircraft densities greater 630 per day. This is mainly because the controller workload gets saturated at that traffic volume per day. Benefits realized in alternatives B, C and D as compared to alternative A increased exponentially at traffic densities greater than 630 i.e. when controller workload for alternative A becomes saturated. / Master of Science

The McMaster incident detection algorithm is being tested on-line within the Burlington freeway traffic management system (FTMS) as an alternative to the existing California-type algorithm currently in place. This paper represents the most recent and comprehensive evaluation of the McMaster algorithm's performance to date. In the past, the algorithm has been tested using single lane detectors for the northbound lanes only. This evaluation uses data from lanes 1 and 2 for each of the 13 northbound and 13 southbound detector stations. The data was collected during a 60-day period beginning on November 15, 1990 and ending January 13, 1991. Detection rate, mean time-lag to detection and false alarm rate are used to evaluate the performance of the algorithm. As well, those factors such as winter precipitation, which influenced the performance of the algorithm are also examined. To improve the algorithm's detection rate and lower its false alarm rate, it is reccomended that the persistence check used to declare an incident be increased by 30-seconds from 2 to 3 periods. / Thesis / Candidate in Philosophy

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. 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. 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. 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. 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. 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. 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. / QC 20101119