Air Traffic Management (ATM) faces significant challenges in ensuring efficiency, safety, and sustainability. Among these, weather conditions and Air Traffic Controller (ATCO) workload play crucial roles in overall system performance. Adverse weather frequently disrupts operations, leading to inefficient flight trajectories, increased fuel consumption, and environmental impact. It also elevates ATCO workload, thereby complicating ATCOs’ ability to maintain safe and efficient air traffic flow. This thesis explores data-driven and analytical approaches to address these challenges, focusing on the impact of weather on flight efficiency, airspace capacity, and ATCO scheduling in remote tower centers. Additionally, it examines ATCO workload prediction using behavioral and physiological data. The study covers applications in airspace capacity management, staff scheduling, and ATCO workload assessment.

The thesis examines historical flight and weather data from Stockholm Arlanda and Gothenburg Landvetter airports over a two-year period (2019–2020), revealing persistent inefficiencies in arrival operations despite the overall reduction in traffic during the COVID-19 pandemic. It presents a methodology grounded in statistical analysis to identify the key factors influencing arrival performance, with particular emphasis on the impact of adverse weather conditions and traffic intensity. The proposed approach systematically determines the most influential variables affecting arrival performance in both the horizontal and vertical flight dimensions.

Adverse weather conditions, such as convective weather, can lead to restrictions on aircraft movements, reduce available routes, and necessitate adjustments in ATM strategies. As a result, understanding and predicting weather-related impacts on airspace capacity is essential for optimizing air traffic flow and minimizing delays. In this thesis, we develop a methodology, based on the continuous maxflow/mincut theory, to estimate reductions in Air Traffic Control (ATC) sector capacity due to predicted convective weather activity. The uncertainty in meteorological forecasts is quantified using Ensemble Weather Forecasting. We demonstrate the application of this methodology for assessing congestion in ATC sectors, using a realistic sector and a full sector configuration as examples. Additionally, we introduce a probabilistic framework for presenting congestion status, aimed at supporting decision-making processes at the Flow Management Position.

The thesis presents probabilistic models that incorporate the impact of adverse weather conditions into a Mixed-Integer Linear Programming framework for ATCO shift scheduling in remote and conventional towers. Building on previous project developments, these models specifically address the influence of weather on ATCO operations in remote towers. Probabilistic weather products are used to generate ensembles of staffing solutions, enabling the derivation of probability distributions for the required number of ATCOs. The modeling approach leverages recently developed techniques to tackle challenges associated with weather uncertainty. The proposed solutions are validated using historical flight and weather data from five Swedish airports designated for future remote operation.

The final part of this thesis focuses on developing unobtrusive methods for predicting ATCO workload by exploring the feasibility of non-intrusive data collection techniques combined with machine learning algorithms. Eye-tracking data, previously identified as a promising indicator of ATCO workload, were collected from controllers in simulated environments and used as predictive features. Subjective workload assessments, based on self-reported Cooper-Harper scale ratings, serve as label variables. Multiple machine learning models are evaluated for workload prediction, and feature selection techniques are applied to identify a minimal yet effective set of eye-tracking features. This approach provides a seamless, non-intrusive means of continuously assessing workload, making it a valuable tool for both research and operational applications in ATC environments.

By addressing critical challenges in ATM, this thesis contributes to a safer, more efficient, and environmentally sustainable air transport system. The findings of this thesis have significant implications for the future of ATM, particularly in an era of increasing air traffic demand and evolving weather challenges. The integration of data-driven techniques, optimization, and probabilistic modeling offers a powerful framework for improving decision-making in ATM. The methodologies proposed in this thesis can serve as a foundation for future research and industry applications, enabling continuous improvements in ATM performance and resilience against external disruptions.

Lufttrafikledning (ATM) står inför betydande utmaningar när det gäl-ler att säkerställa effektivitet, säkerhet och hållbarhet. Väderförhål-landen och flygtrafikledarnas (ATCO) arbetsbelastning spelar en avgörande roll för det övergripande systemets prestanda. Ogynnsamma väderförhållanden stör ofta verksamheten, vilket leder till ineffektiva flygvägar, ökad bränsleförbrukning och miljöpåverkan. Det medför även en ökad arbetsbelastning för ATCO, vilket försvårar deras förmåga att upprätthålla ett säkert och effektivt trafikflöde. Denna avhandling undersöker datadrivna och analytiska metoder för att han-tera dessa utmaningar, med fokus på vädrets inverkan på flygeffektivitet, luftrumskapacitet och ATCO-planering i fjärrstyrda torncentraler. Dessutom analyseras ATCO-arbetsbelastningsprognoser baserade på beteendemässiga och fysiologiska data. Studien omfattar tillämpningar inom luftrumskapacitetshantering, personalplanering och bedömning av ATCO:s arbetsbelastning.

Studien analyserar historiska flyg- och väderdata från Stockholm Arlanda och Göteborg Landvetter flygplatser under en tvåårsperiod (2019–2020) och belyser kvarstående ineffektivitet trots minskad trafik under COVID-19-pandemin. Denna avhandling presenterar en metodik baserad på statistisk analys för att identifiera de viktigaste faktorerna som påverkar olika aspekter av ankomstprestanda, med särskilt fokus på effekterna av ogynnsamt väder och trafikintensitet. Den föreslagna metoden identifierar specifikt de mest betydande faktorerna som påverkar ankomstprestanda i både horisontella och vertikala dimensioner.

Ogynnsamma väderförhållanden, såsom konvektivt väder, kan leda till restriktioner för flygrörelser, minska tillgängliga rutter och kräva justeringar av ATM-strategier. Därför är det avgörande att förstå och förutsäga väderrelaterade effekter på luftrumskapaciteten för att optimera lufttrafikflödet och minimera förseningar. I denna avhandling utvecklar vi en metodik, baserad på den kontinuerliga maxflow/mincut-teorin, för att uppskatta minskningar i flygtrafikled-ningens (ATC) sektorkapacitet till följd av förutspådd konvektiv väderaktivitet. Osäkerheten i meteorologiska prognoser kvantifieras med hjälp av ensembleväderprognoser. Vi demonstrerar tillämpningen av denna metodik för att bedöma trängsel i ATC-sektorer, med exempel på en realistisk sektor och en fullständig sektorkonfiguration. Vi introducerar dessutom ett probabilistiskt ramverk för att presentera trängselstatus, med syfte att stödja beslutsprocesser vid flödeshanteringspositionen.

Studien presenterar probabilistiska modeller som integrerar effekten av ogynnsamma väderförhållanden i ett blandat heltalslinjärt optimeringsramverk för ATCO-skift-schemaläggning i både fjärrstyrda och konventionella torn. Dessa modeller hanterar specifikt vädrets inverkan på ATCO:s arbete i fjärrstyrda torn genom att bygga vidare på tidigare projektutvecklingar. Probabilistiska väderprodukter används för att generera ensemblelösningar för bemanning, vilket möjliggör härledning av sannolikhetsfördelningar för det nödvändiga antalet ATCO:er. Denna modellansats utnyttjar nyligen utvecklade tekniker för att hantera utmaningar kopplade till väderosäkerhet. De föreslagna lösningarna valideras med hjälp av historiska flyg- och väderdata från fem svenska flygplatser som är utpekade för framtida fjärrstyrd drift.

Den sista delen av denna avhandling fokuserar på att utveckla diskreta metoder för att förutsäga ATCO:s arbetsbelastning genom att undersöka möjligheterna med icke-intrusiva datainsamlingstekniker i kombination med maskininlärningsalgoritmer. Ögonrörelsedata, som tidigare har identifierats som en lovande indikator för ATCO:s arbetsbelastning, samlades in från flygtrafikledare i simulerade miljöer och användes som prediktiva variabler. Subjektiva arbetsbelastnings-bedömningar, baserade på självskattade Cooper-Harper-skattningar, användes som målvariabler. Flera maskininlärningsmodeller utvärderades för att förutsäga arbetsbelastning, och tekniker för variabelurval tillämpades för att identifiera en minimal men effektiv uppsättning av ögonrörelsevariabler. Denna metod möjliggör en sömlös och icke-intrusiv kontinuerlig bedömning av arbetsbelastning, vilket gör den till ett värdefullt verktyg både för forskning och operativa tillämpningar inom flygtrafikledning.

Denna avhandling bidrar till ett säkrare, mer effektivt och miljömässigt hållbart lufttransportsystem genom att hantera kritiska utmaningar inom ATM. Resultaten har stor betydelse för framtidens ATM, särskilt i en tid med ökande efterfrågan på lufttrafik och föränderliga väderutmaningar. Integrationen av datadrivna tekniker, optimering och probabilistisk modellering erbjuder ett kraftfullt ramverk för att förbättra beslutsfattandet inom ATM. De metoder som föreslås i denna avhandling kan fungera som en grund för framtida forskning och industriella tillämpningar, vilket möjliggör kontinuerliga förbättringar av ATM:s prestanda och motståndskraft mot externa störningar.

We consider two very basic problems – one in unsupervisedand one in supervised learning. In the former, we are given a set ofpoints and have to label half of the points red and half the points blueso as to maximize the red–blue separation, i.e., the length of a shortestbichromatic edge. In the latter, the data (points in the plane) are alreadylabeled red and blue, and we seek a linear classifier (a separator of thetwo given point sets) that can be described using the smallest integers.We give algorithms for both problems. Our solutions are simple; themain contribution of the paper is highlighting the problems and theiralgorithmic solutions, which, to our knowledge, have not been presentedpreviously, despite the problems being fundamental to the field. We alsoconsider related problems.

In this paper, we examine the feasibility of assessing air traffic controller (ATCO) workload using non-intrusive eye-tracking measures and machine learning algorithms. A total of N = 18 ATCOs participated in simulator runs with tasks inducing three task-load levels: light, moderate, and heavy. Task load was modulated through traffic load and the associated increase in complexity. We collected eye-tracking data (statistical summaries of which serve as features) and obtained subjective workload assessments using self-reported Cooper-Harper Scale scores, which act as label variables. We evaluate the performance of eight classical machine learning models, with the k-nearest neighbors and support vector classifier models emerging as the most promising. To optimize performance, we apply feature selection techniques, focusing on these best-performing models. Feature selection via recursive feature elimination (RFE) based on permutation importance reduces the original 42 features while maintaining or improving performance. The outcomes yield promising results in workload-level estimation, achieving an F1 score of 0.870 for low/high workload prediction and an F1 score of 0.788 for predicting three different levels of workload. The RFE process identifies optimal feature sets ranging from 7 to 13 features for different tasks, with minimal impact on performance. A "knee point" is observed, representing the optimal balance between model performance and dimensionality. Adding more features beyond this point contributes little to performance improvement while increasing model complexity. These findings indicate that even a few features can be sufficient for accurate workload prediction. We show that head-movement features provide valuable information. Comparable performance is achieved using only ocular features, but this requires more features. Asymmetry in left and right eye metrics holds workload-related information but transforming them into averages and differences reduces performance. Retaining the original features separately is the most effective approach, incorporating their absolute differences may provide slight benefits in certain models.

In this paper, we examine the feasibility of assessing air traffic controller (ATCO) workload (WL) using non-intrusive eye-tracking measures and machine learning (ML) algorithms. We concurrently acquire electroencephalography (EEG) data from a workloadoptimized wearable device and subjective WL assessments through self-reported Cooper-Harper scale (CHS) workload-rating scores, employing both as label variables. A sample of n = 18 ATCOs participate in simulated work sessions encompassing tasks designed to induce three distinct task-load levels: light, moderate, and heavy. We evaluate the performance of five classical ML models. Focusing on the best-performing models, we apply feature selection techniques to identify reduced sets of eye-tracking features. Starting with 58 features, we use a recursive elimination method based on permutation importance, aiming to determine the minimal feature set while also striving for improved performance. The outcomes yielded promising results in the realm of workloadlevel estimation, achieving 96% accuracy (f1-score=0.87) with 34 features for high workload prediction and 88% accuracy (f1-score=0.82) using 57 features in predicting 3 different levels of workload. We further reduced the feature sets to 6-13 features for different tasks with minimal impact on performance. We identified a \x93knee point\x94 as the optimal balance between model performance and dimensionality. Adding more features beyond this point did little to improve performance, but increased model complexity. These results indicate that even a small number (less than 10) of features can be sufficient for WL prediction.

Adverse weather is the primary cause of delays to air traffic. In this paper models of different maturity level from the United States, Canada and Europe are compared to derive best practices in how to mitigate these impacts. The models are illustrated through case studies in each one of these airspaces. An example in Jacksonville Center in Florida, one for Toronto Airport and one for the Rhein Airspace adjacent to Munich Airport are presented here. Lastly, some of the modeling characteristics are compared to derive best practices and lesson learned that can be leveraged from each other.

The rapid expansion of Unmanned Aircraft (UA) usage has increased the need for reliable collision avoidance systems. This paper presents a trust-based, sensor-free collision avoidance system for UAs, leveraging the Drone Remote Identification Protocol (DRIP) to establish trust between aircraft. The proposed system uses a geometric-based cooperative avoidance method to optimize efficiency and a fail-safe repulsion avoidance mechanism for enhanced safety. The proposed system's effectiveness is evaluated through a series of simulations and real-world tests, focusing on metrics such as safety, flight distance, flight time, and acceleration requirements. The results are promising, indicating that the trust-based approach may successfully balance efficiency and safety, providing insights into potential use cases for DRIP.

We propose a new approach to evaluate ocular measurements of air traffic controllers (ATCOs) as potential workload and fatigue indicators. We employ the Fast Fourier transform (FFT) to test our assumption that humans respond to increasing fatigue with harmonic oscillations in the eye movement, while they respond to increasingly high workload with disruptions to these harmonic oscillations. The FFT yields the frequency spectrum and we suggest to use the center of gravity of this spectrum to capture the variations. We give a proof-of concept study to evaluate our approach and we were able to verify our hypotheses in some cases, in particular, we identify the fixation duration as a promising indicator of changes in workload.

Weather has a strong impact on Air Traffic Management (ATM). Inefficient weather avoidance procedures and inaccurate prognosis lead to longer aircraft routes and, as a result, to fuel waste and increased negative environmental impact. A better integration of weather information into the operational ATM-system will ultimately improve the overall air traffic safety and efficiency. Covid-19 pandemics affected aviation severely, resulting in an unprecedented reduction of air traffic, and gave the opportunity to study the flight performance in non-congested scenarios. We investigated the historical flight and weather data from Stockholm Arlanda and Gothenburg Landvetter airports for the period of two years 2019 and 2020 and discovered noticeable inefficiencies and environmental performance degradation, which persisted despite significant reduction of traffic intensity in March 2020. This thesis proposes a methodology that allows to distinguish which factors have the highest impact on which aspects of arrival performance in horizontal and vertical dimensions.

Academic Excellence in ATM and UTM Research (AEAR) group operating within the Communications and Transport Systems (KTS) division in Linköping University (LIU), together with the Research and Development at Luftfartsverket (LFV, Swedish Air Navigation Service Provider (ANSP)) develops optimization techniques to support efficient decision-making for aviation authorities. In this thesis, we design probabilistic models, which take into account the influence of bad weather conditions on the solutions developed in the related project and integrate them into the corresponding optimization framework. Probabilistic models were applied to account for weather impact on Air Traffic Controller (ATCO) work in remote and conventional towers. The probabilistic weather products were used to obtain an ensemble of staffing solutions, from which the probability distributions of the number of necessary ATCOs were derived. The modelling is based on the techniques recently developed within several Single European Sky ATM Research (SESAR) projects addressing weather uncertainty challenges. The proposed solution was successfully tested using the historical flight and weather data from five airports in Sweden planned for remote operation in the future.

The paper focuses on the performance assessment of the arrival operations in Oslo Gardermoen airport implementing point merge (PM) procedures. We take a data-driven approach based on the open-source ADS-B data, and conduct a detailed performance assessment utilizing a diverse set of performance indicators, including newly developed metrics for better understanding of the PM specifics. The results of the performance evaluation indicate that the PM systems are currently underutilized in Oslo airport, and their increased usage may lead to the improved arrival performance, especially during the peak time periods.