Journal of Open Aviation Science is an open-access peer-reviewed journal, established by researchers for researchers. It promotes high quality open science, open data and reproducible scientific research for aviation. In addition to original scientific research, we welcome replication of existing research publications using open source implementations and open data. Particularly, we welcome contributions comparing the performance of reference models with their open source counterparts.
Reviews and Responses for A Methodology for Quantifying Response Times for Deconfliction Actions Through ATC Communications
Detailed reviews and responses are available in the PDF and HTML versions of this document. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8462
Copyright (c) 2026 Timothe Krauth, Kim Gaume, Xavier Olive, Junzi Sun, Gabriel Jarry, Allan Tart, Lucie Smetanová https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Aerodrome Movement Monitoring Using ADS-B Data: A Case Study at Lommis Airfield
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8455
Copyright (c) 2026 Alex Fustagueras, Manuel Waltert, Àlex Vallés Villanova, Junzi Sun, Anastasia Lemetti https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Can YouTube Stream Recordings Improve Speech Recognition for Air Traffic Control?
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8477
Copyright (c) 2026 Niclas Wüstenbecker, Oliver Ohneiser, Matthias Kleinert, Allan Tart, Max Li https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Generation of Vertical Profiles with Neural Ordinary Differential Equations Trained on Open Trajectory Data
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8457
Copyright (c) 2026 Gabriel Jarry, Xavier Olive, Raúl Sáez, Manuel Waltert, Yutong Chen https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Generative Short-Term Aircraft Trajectory Prediction with Conditional Flow Matching
Detailed reviews and responses are available in the PDF and HTML versions of this document. https://doi.org/10.59490/joas.2026.8468
Copyright (c) 2026 Benoit Figuet, Timothé Krauth, Steve Barry, Gabriel Jarry, Richard Alligier, Max Li https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Identifying Orthodromy and Loxodromy Segments in ADS-B Aircraft Trajectory Data
Detailed reviews and responses are available in the PDF and HTML versions of this document. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8466
Copyright (c) 2026 Richard Alligier, Kim Gaume, David Gianazza, Xavier Olive, Nicolas Durand https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Methodology to Quantify and Attribute ATM Inefficiencies and Their Impact on Fuel Consumption
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8469
Copyright (c) 2026 Gabriele Sirtori, Laurent Joly, Melissa Hofmann, Raúl Sáez, Max Li https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Prediction of Arrival Runway Occupancy Time and Exit Taxiway Using ADS-B Trajectories
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8465
Copyright (c) 2026 Kevin Hänggi, Manuel Waltert, Jeremy Wilde, Gabriel Jarry, Yutong Chen https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Review of ADS-B Data Usage with the Focus on Data Cleaning
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8467
Copyright (c) 2026 Ruolan Ren, Jingcheng Zhong, Dizhi Guo, Ruixin Wang, Christophe Hurter, Ramon Dalmau, Enrico Spinielli https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Understanding Citizen Science: Insights from the 2024/2025 OpenSky Network User Survey
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8474
Copyright (c) 2026 Janina Inauen, Vincent Lenders, Karsten Donnay, Martin Strohmeier, Ramon Dalmau, Celina Vetter https://creativecommons.org/licenses/by/4.0
Reviews and Responses for Vertical Efficiency Analysis of Continuous Climb and Descent Operations in Canadian Terminal
Detailed reviews and responses are in the PDF or HTML files. The DOI for the original paper is https://doi.org/10.59490/joas.2026.8470
Copyright (c) 2026 Guilherme Trindade Tolentino Bernardo, Marcelo Xavier Guterres, Alexandre de Barros, Tatiana Polishchuk, Raúl Sáez, Ryota Mori https://creativecommons.org/licenses/by/4.0
A Methodology for Quantifying Response Times for Deconfliction Actions Through ATC Communications
The reaction time to a deconfliction situation refers to the interval between detecting a potential loss of separation and taking corrective action. For air traffic controllers, it represents the lead time between identifying a conflict and issuing a deconfliction instruction to a pilot. For pilots, it corresponds to the delay between receiving an ATC clearance and initiating the associated maneuver. Because both processes are influenced by human factors, these response times constitute a significant source of uncertainty in Air Traffic Management. While the controller’s reaction time is particularly difficult to estimate since the exact moment at which a conflict is cognitively detected cannot be directly inferred from operational data, this paper focuses on quantifying pilot response times. To this end, we propose a methodology that combines Natural Language Processing techniques with surveillance and flight plan data. ATC–pilot voice communications are transcribed using a fine-tuned Automatic Speech Recognition model, and aircraft callsigns are identified through Named Entity Recognition. The transcriptions are then matched with corresponding flights in ADS-B surveillance data. Using flight plans, we identify lateral deconfliction maneuvers and align them temporally with the preceding ATC clearances to estimate the elapsed time between instruction and execution. Because the approach depends on a sequence of emerging algorithms whose robustness is still evolving, the study focuses on identifying the conditions under which the methodology performs reliably, highlighting its current limitations and associated data challenges, and proposing ways to overcome them.
Copyright (c) 2026 Timothe Krauth, Kim Gaume, Xavier Olive, Junzi Sun https://creativecommons.org/licenses/by/4.0
Aerodrome Movement Monitoring Using ADS-B Data: A Case Study at Lommis Airfield
This study presents and validates a machine learning pipeline that transforms raw Automatic Dependent Surveillance-Broadcast (ADS-B) data into structured aerodrome movement reports, addressing regulatory needs for continuous monitoring of aircraft operations at small, non-towered airfields. The approach automatically identifies aerodrome-specific flight events, particularly repetitive traffic circuits, which constitute a significant portion of General Aviation traffic at such airfields. Using ADS-B data observed at Lommis Airfield, a representative regional airfield in Switzerland, we filtered and preprocessed raw flight trajectories and segmented those meeting the filtering criteria into aerodrome circuit candidates. We then formulated circuit detection as a supervised binary classification problem and compared five machine learning approaches: Logistic Regression, Random Forest, unidirectional and bidirectional Long Short-Term Memory (LSTM) networks, and a 1D Convolutional Neural Network (CNN). Each traffic circuit candidate was characterised by eight engineered features capturing kinematic and flight-phase information. The 1D CNN model achieved 99.15% accuracy, outperforming rule-based heuristics by 25.5 percentage points in recall, while simpler models (Logistic Regression, Random Forest) reached comparable performance with higher interpretability and efficiency. End-to-end validation of the proposed pipeline over a three-month period yielded 67.6% overall detection coverage (438 of 648 flights), limited primarily by ADS-B data availability rather than model performance. The validated pipeline demonstrates the potential for a scalable path toward automated, data-driven movement reporting, with full end-to-end validation conducted at a single airfield and additional cross-airfield evidence shown at the model level.
Copyright (c) 2026 Alex Fustagueras, Manuel Waltert https://creativecommons.org/licenses/by/4.0
Generation of Vertical Profiles with Neural Ordinary Differential Equations Trained on Open Trajectory Data
Recent advances in Neural Ordinary Differential Equations (Neural ODE) have shown that high-fidelity aircraft dynamics can be learned from flight recorder data, but such proprietary datasets remain largely inaccessible. In this study, we extend these principles to open trajectory data by training a Neural ODE model on Automatic Dependent Surveillance–Broadcast (ADS-B) and Mode~S Enhanced Surveillance (EHS) information retrieved from the OpenSky Network. The model focuses on reconstructing the vertical dynamics of transport aircraft using only openly available surveillance variables. The resulting framework learns continuous-time dynamics that remain physically consistent through embedded kinematic relations, demonstrating that realistic vertical profiles can be generated solely from open surveillance data. This work contributes to reproducible, data-driven performance modelling and supports the broader adoption of open, physics-guided learning methods in aviation research.
Copyright (c) 2026 Gabriel Jarry, Xavier Olive https://creativecommons.org/licenses/by/4.0
Can YouTube Stream Recordings Improve Speech Recognition for Air Traffic Control?
Automatic speech recognition for air traffic control (ATC) faces severe training data scarcity due to operational recording restrictions and expensive domain-expert transcription requirements. We address this limitation by developing an automated pipeline that extracts large-scale, high-quality training data from publicly available YouTube streams of virtual ATC simulator sessions from networks such as VATSIM and IVAO. Our approach systematically processes over 2,000 hours of content spanning 709 videos from virtual airports and airspaces in 17 countries across multiple continents, operational domains (ground, tower, approach, en-route), and diverse speaker accents. The pipeline employs speaker diarization for utterance segmentation, parallel transcription using three complementary automatic speech recognition (ASR) architectures with distinct error characteristics, and Large Language Model-based transcript fusion that synthesizes improved pseudo-labels while filtering non-ATC content. Manual verification on a stratified 120-minute evaluation set demonstrates 10.2% word error rate for controller speech and 18.3% for pilot speech—representing 37% relative improvement over the best individual model and establishing pseudo-label quality sufficient for downstream model training. We show the feasibility of this approach by training a compact 115M-parameter ASR model exclusively on automatically generated transcripts without any manually annotated operational data. Evaluation on the operational ATCO2 benchmark reveals 21.1% word error rate compared to 35.6% for published baselines trained on smaller manually-transcribed datasets, despite the domain gap between virtual and operational ATC, while achieving approximately five times faster inference. These results demonstrate that large-scale geographically and acoustically diverse, pseudo-labeled data can effectively compensate for moderate label noise when training specialized-domain speech recognition systems. We openly release the complete processing pipeline, curated video collection, and our trained model to enable reproducible research.
Copyright (c) 2026 Niclas Wüstenbecker, Oliver Ohneiser, Matthias Kleinert https://creativecommons.org/licenses/by/4.0
Generative Short-Term Aircraft Trajectory Prediction with Conditional Flow Matching
Reliable short-term aircraft trajectory prediction is essential for safety and efficiency in Air Traffic Management (ATM). This work introduces a generative framework for probabilistic 4D trajectory forecasting based on Conditional Flow Matching (CFM), a recent deep generative modeling approach that combines stable likelihood-based training with efficient sampling. The model is trained on historical ADS–B data from the OpenSky Network to predict aircraft motion over a 60 s horizon, conditioned on the preceding 60 s of observations. The model generates ensembles of realistic future trajectories that capture the inherent uncertainty of aircraft motion and enable probabilistic assessment of potential conflicts. As an application, we estimate the probability of mid-air collision during a loss-of-separation event using Monte Carlo simulation over the generated trajectories, providing a quantitative risk measure. The results demonstrate that flow-based generative modeling offers a principled foundation for uncertainty-aware trajectory prediction and safety analysis in ATM.
Copyright (c) 2026 Benoit Figuet, Timothé Krauth, Steve Barry https://creativecommons.org/licenses/by/4.0
Identifying Orthodromy and Loxodromy Segments in ADS-B Aircraft Trajectory Data
Aircraft trajectories can often be interpreted in terms of two fundamental path types: orthodromies (great-circle routes, representing the shortest path on the globe) and loxodromies (constant track-angle paths). While great circles typically define the reality of intercontinental flight planning, operational constraints, such as waypoint routing and air traffic control instructions, mean that actual trajectories are composed of multiple segments, not always conforming neatly to one type or the other. Recognizing which segments correspond to orthodromies or loxodromies can provide semantic meaning to flight data, transforming raw positional information into interpretable patterns of pilot intent or controller intervention. We present a method to automatically identify these trajectory segments without any prior knowledge about waypoints. The approach leverages map projections: with the Mercator projection, by construction, loxodromies appear as straight lines, while orthodromies do so in gnomonic projections. By detecting straight-line portions in these transformed trajectories, we can classify corresponding segments of the original flight path. This may enable the identification of even short segments, offering insights into flight planning and deconfliction manoeuvres—for example, detecting when an aircraft temporarily deviates from its planned orthodromic route under controller instruction. Using an open trajectories dataset where 13% of the total duration is labeled as deconfliction maneuvers, the identified loxodromies have a duration of 2% of the total duration while 39% of these 2% can be matched with deconfliction maneuvers, higher than the 13% prior.
Methodology to Quantify and Attribute ATM Inefficiencies and Their Impact on Fuel Consumption
Air Traffic Management (ATM) is a critical component of the European air transport system; its efficiency has a significant impact on the airlines’ profitability but is also a lever of the pathway towards aviation sustainability, with an expected contribution up to 8% in reducing aviation-related emissions between now and 2050. In this context, the presented work has the ambition of developing a methodology to quantify and question the aforementioned efficiency gain potential, and to categorize inefficiencies in the ATM. This paper details the initial workflow referring to one among a small number of city pairs that have been selected to cover routes of various lengths departing from Europe. Later, the complete subset of city pairs, plus broader, statistically relevant study cases will be assessed. In this perspective and as the basis of a synthetic efficiency indicator, key metrics, targeting vertical, horizontal and combined flight efficiency are revisited to contribute to the quantification of ATM inefficiency. The insight offered by these metrics enables tracking the efficiency evolution with the update of flight plans and along the complete flight execution, rather than focusing on a specific flight phase.
Copyright (c) 2026 Gabriele Sirtori, Laurent Joly, Melissa Hofmann https://creativecommons.org/licenses/by/4.0
Prediction of Arrival Runway Occupancy Time and Exit Taxiway Using ADS-B Trajectories
The expected future growth in aircraft movements will require airports to increase runway capacity, which is often constrained, among other factors, by aircraft arrival runway occupancy time (AROT) and by the rapid exit taxiway (RET) selected by pilots. Existing prediction approaches for AROT and RET selection rely mostly on proprietary Advanced Surface Movement Guidance and Control System (A-SMGCS) or radar data and often ignore temporal context in trajectory patterns, leaving gaps for operationally relevant applications. In this study, we used Automatic Dependent Surveillance–Broadcast (ADS-B) trajectory data sourced from the OpenSky Network for flights arriving at Zurich Airport in the years 2024–2025 to train two machine learning models: a LightGBM model for RET prediction and a neural network combining time-invariant features with time-variant ADS-B trajectory snippets for AROT prediction. The results of both models were within the range reported in the literature. The RET prediction model achieved a weighted accuracy of 79.4%, while the AROT prediction model yielded a mean absolute error of 3.95 s and a root mean square error of 5.01 s. These findings demonstrate that ADS-B-based models can support air traffic controllers in reducing separation between arriving aircraft, thereby potentially enhancing runway arrival throughput at aerodromes.
Copyright (c) 2026 Kevin Hänggi, Manuel Waltert, Jeremy Wilde https://creativecommons.org/licenses/by/4.0
Review of ADS-B Data Usage with the focus on Data Cleaning
Automatic Dependent Surveillance–Broadcast (ADS-B) data have become a vital resource for research on trajectory prediction, conflict detection, and air traffic management. However, due to limitations in data acquisition and transmission, ADS-B datasets often contain missing points, irregular sampling, and anomalies. To ensure usability, researchers typically apply data cleaning and preprocessing, which improve data quality but may alter original characteristics and cause deviations between algorithm outputs and real operational patterns. Existing studies largely focus on individual cleaning methods, lacking systematic and quantitative assessments of their impact on downstream applications. To address this gap, this study systematically investigates the relationship between data cleaning and algorithmic performance in ADS-B analytics. It reviews major ADS-B applications and prevalent cleaning techniques, summarizes typical preprocessing pipelines, and provides guidance for building more robust evaluation frameworks. An AutoEncoder (AE)-based experiment is conducted using three architecturally distinct autoencoders (Fully Connected AE (FC-AE), Long Short-Term Memory AE (LSTM-AE), Gated Recurrent Unit AE (GRU-AE)) across four geographically diverse airport datasets, with trajectories contaminated by Gaussian, drift, spike, and missing data noise to assess the influence of cleaning strategies on trajectory reconstruction. Results indicate that spike noise has the least impact on reconstruction performance across all model and dataset combinations, while the relative sensitivity to Gaussian, drift, and missing data noise varies primarily with dataset characteristics rather than model architecture. These findings suggest that data cleaning priorities should be informed by the specific noise profile of the operational environment rather than applied uniformly.
Copyright (c) 2026 Ruolan Ren, Jingcheng Zhong, Dizhi Guo, Ruixin Wang, Christophe Hurter https://creativecommons.org/licenses/by/4.0
Understanding Citizen Science: Insights from the 2024/2025 OpenSky Network User Survey
The OpenSky Network has grown into one of the largest community-driven air traffic surveillance infrastructures worldwide. While its data are widely used in research, comparatively little is known about the individuals who maintain the sensor network. In late 2024 to early 2025, we conducted the first large-scale user survey of OpenSky Network members to better understand the demographics, motivations, and barriers associated with participation. This paper presents key findings from more than 500 responses, highlighting who contributes to OpenSky and why, and what prevents others from becoming active feeders. The collected data show that participants are predominantly well-educated, above-median-income males from Western countries, with an approximate average age of 50 years. Their primary motivation is contributing to research, despite limited knowledge of the specific research projects. Cost is the main barrier to participation, particularly in underrepresented regions, whereas disinterest and environmental concerns deter adoption in other areas.
Copyright (c) 2026 Janina Inauen, Vincent Lenders, Karsten Donnay, Martin Strohmeier https://creativecommons.org/licenses/by/4.0
Vertical Efficiency Analysis of Continuous Climb and Descent Operations in Canadian Terminal Areas
This work presents a data-driven framework that links ADS-B surveillance data with ICAO GANP vertical efficiency KPIs to evaluate climb and descent performance across ten Canada’s major airports. Based on an analysis of 158,660 departure flights and 155,840 arrival flights crossing Canadian airspace, this study characterizes patterns of vertical flight efficiency across major Canadian airports. Rather than attributing causality, the results highlight systematic differences in efficiency across airports and over time, including variations observed during the winter–spring transition period covered by the dataset. Differences between western airports (e.g., Vancouver area) and eastern airports (e.g., Toronto, Montréal, Québec) are also observed, reflecting distinct operational and environmental contexts. The findings therefore emphasize the role of traffic intensity, regional operating environments, and short-term temporal variability in shaping observed vertical efficiency outcomes. Vancouver shows greater inefficiency during the climb phase than Montréal and Toronto. At both eastern hubs, climb times increased by 105% from January to April 2025, coinciding with the seasonal transition from winter to spring. Descent phases are generally less efficient than climbs, especially at Vancouver and Toronto, where level-off durations frequently exceed five minutes. Montréal and Ottawa also show notable inefficiencies, with average durations exceeding three minutes. Calgary, benefiting from less congested airspace, shows better overall performance compared to the other major hubs, while Winnipeg demonstrates efficient descents but inefficient climbs. Targeted measures such as segregated arrival/departure corridors and time-based metering at Vancouver and Toronto are suggested to improve vertical efficiency. This research contributes to establishing a versatile tool for continuous ATM performance monitoring and collaborative optimization across international boundaries.
Copyright (c) 2026 Guilherme Trindade Tolentino Bernardo, Marcelo Xavier Guterres, Alexandre de Barros https://creativecommons.org/licenses/by/4.0