J. Sun
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34 records found
1
Within the complex European Air Traffic Management (ATM) network, Letters of Agreement (LOAs) are critical regulatory instruments primarily put in place to ensure operational safety during tactical sector handovers. However, as the industry transitions toward Trajectory-Based Operations (TBO), these vital safety documents remain trapped in publicly unavailable text formats, masking systemic flight inefficiencies and hindering compliance automation. To bridge this gap, this paper introduces a data-driven, reproducible trajectory processing pipeline designed to systematically reverse-engineer LOA constraints directly from historical flight data. The methodology employs a two-stage framework leveraging both classification and regression layers. Target entry/exit clusters, isolated via an unsupervised Hierarchical DB-SCAN (HDBSCAN) framework, are first modeled using an Extreme Gradient Boosting (XGBoost) classifier. The resulting predictions feed into a decoupled ensemble of multi-target XGBoost regressors to evaluate positional and efficiency targets. Model performance is validated against a 20% test split, and the final outputs are interpreted through game-theoretic SHapley Additive exPlanations (SHAP) analysis and a rule extractor to isolate primary operational drivers. Evaluated over a full 28-day AIRAC cycle, the architecture achieves exceptional spatial coordinate projection accuracy, demonstrating high 𝑅2 scores of 0.933 for latitude and 0.951 for longitude at sector entry points. The empirical results successfully reconstruct 95% confidence spatial boundary ellipsoids and identify (un)written hidden rules routinely executed by air traffic controllers. Ultimately, these discovered constraints are integrated into an interactive, open-source visualization dashboard, establishing a scalable digital twin framework to support real-time safety monitoring, flight plan compliance verification, and data-driven airspace redesign simulations.
...
Within the complex European Air Traffic Management (ATM) network, Letters of Agreement (LOAs) are critical regulatory instruments primarily put in place to ensure operational safety during tactical sector handovers. However, as the industry transitions toward Trajectory-Based Operations (TBO), these vital safety documents remain trapped in publicly unavailable text formats, masking systemic flight inefficiencies and hindering compliance automation. To bridge this gap, this paper introduces a data-driven, reproducible trajectory processing pipeline designed to systematically reverse-engineer LOA constraints directly from historical flight data. The methodology employs a two-stage framework leveraging both classification and regression layers. Target entry/exit clusters, isolated via an unsupervised Hierarchical DB-SCAN (HDBSCAN) framework, are first modeled using an Extreme Gradient Boosting (XGBoost) classifier. The resulting predictions feed into a decoupled ensemble of multi-target XGBoost regressors to evaluate positional and efficiency targets. Model performance is validated against a 20% test split, and the final outputs are interpreted through game-theoretic SHapley Additive exPlanations (SHAP) analysis and a rule extractor to isolate primary operational drivers. Evaluated over a full 28-day AIRAC cycle, the architecture achieves exceptional spatial coordinate projection accuracy, demonstrating high 𝑅2 scores of 0.933 for latitude and 0.951 for longitude at sector entry points. The empirical results successfully reconstruct 95% confidence spatial boundary ellipsoids and identify (un)written hidden rules routinely executed by air traffic controllers. Ultimately, these discovered constraints are integrated into an interactive, open-source visualization dashboard, establishing a scalable digital twin framework to support real-time safety monitoring, flight plan compliance verification, and data-driven airspace redesign simulations.
Master thesis
(2026)
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J.A.J. Huigen, J. Ellerbroek, M.J. Ribeiro, J. Sun, Ferdinand Dijkstra, P. Proesmans
Continuous Descent Operations (CDOs) can reduce fuel consumption and CO2 emissions. However, their implementation in constrained airspace is often limited by operational procedures and altitude restrictions. Previous studies have evaluated CDO performance under idealised conditions, resulting in insufficient quantification of the effects of real operational constraints. This study investigates how operational constraints on arrival routes influence aircraft vertical descent profiles and the resulting fuel consumption. A framework is introduced that is capable of quantifying fuel consumption across different descent trajectories. The simulation-based framework enables the analysis of incremental modifications to operational restrictions, including changes to level-off altitude and duration. The methodology is applied to arrival traffic at Schiphol Airport, using a dataset of over 8,000 recorded arrivals from the Aircraft Condition Monitoring System (ACMS) and one month of Eurocontrol Demand Data Repository (DDR) traffic data. Level-off segments between Top of Descent (TOD) and the Initial Approach Fix (IAF) are identified and linked to waypoint-based restrictions specified in Letters of Agreement (LoAs) and the Route Availability Document (RAD). The BlueSky air traffic simulator and the Base of Aircraft Data (BADA) 3.16 performance model are used to quantify the fuel impact of modified descent scenarios. The results show a clear relationship between level-off duration, altitude constraints, and fuel consumption. Higher level-off altitudes and shorter durations consistently reduce fuel burn. Regression analysis of recorded flights confirms these trends. A Key Performance Indicator (KPI) based route assessment identifies arrival routes with the greatest potential to reduce fuel consumption, while taking into account operational complexity. The findings show that measurable fuel savings can be achieved through targeted adjustments in airspace restrictions without requiring a complete redesign of the airspace.
...
...
Continuous Descent Operations (CDOs) can reduce fuel consumption and CO2 emissions. However, their implementation in constrained airspace is often limited by operational procedures and altitude restrictions. Previous studies have evaluated CDO performance under idealised conditions, resulting in insufficient quantification of the effects of real operational constraints. This study investigates how operational constraints on arrival routes influence aircraft vertical descent profiles and the resulting fuel consumption. A framework is introduced that is capable of quantifying fuel consumption across different descent trajectories. The simulation-based framework enables the analysis of incremental modifications to operational restrictions, including changes to level-off altitude and duration. The methodology is applied to arrival traffic at Schiphol Airport, using a dataset of over 8,000 recorded arrivals from the Aircraft Condition Monitoring System (ACMS) and one month of Eurocontrol Demand Data Repository (DDR) traffic data. Level-off segments between Top of Descent (TOD) and the Initial Approach Fix (IAF) are identified and linked to waypoint-based restrictions specified in Letters of Agreement (LoAs) and the Route Availability Document (RAD). The BlueSky air traffic simulator and the Base of Aircraft Data (BADA) 3.16 performance model are used to quantify the fuel impact of modified descent scenarios. The results show a clear relationship between level-off duration, altitude constraints, and fuel consumption. Higher level-off altitudes and shorter durations consistently reduce fuel burn. Regression analysis of recorded flights confirms these trends. A Key Performance Indicator (KPI) based route assessment identifies arrival routes with the greatest potential to reduce fuel consumption, while taking into account operational complexity. The findings show that measurable fuel savings can be achieved through targeted adjustments in airspace restrictions without requiring a complete redesign of the airspace.
Master thesis
(2026)
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E. Süülker, I.I. de Pater, M.J. Ribeiro, J. Sun, P.C. Roling, J. de Wilde, A. Piva, P.R.J.R. Lothaller
In the approach phase of a flight, aircraft transitions from the en-route phase to the final approach. The transit time between these phases is highly uncertain and frequently subject to delays. Efficient and reliable prediction of this time is essential for airline fuel planning and flight scheduling, yet current practice still relies largely on fixed deterministic buffers. Most existing work on arrival delay prediction focuses on deterministic models and aggregate indicators (e.g the difference between planned and actual arrival times), often at forecast horizons during the airborne phase. This paper develops and validates an explainable probabilistic forecasting model for flight duration within the Amsterdam Schiphol (AMS) Flight Information Region (FIR), with a forecast moment in the pre-departure phase. The primary objective is to forecast the duration within the AMS FIR using information available at planning while providing interpretable contributors of delay that can be clearly communicated to flight dispatchers and pilots. The study uses a historical operational dataset of roughly 280,000 inbound flights, combining airline planning data, AMS traffic data, and METAR/TAF weather reports. On the held-out test set, the model achieves a reduced MAE of 33% and a reduced RMSE of 26% relative to the current operational baseline, while capturing about one third of the variance in FIR duration (R2= 0.33). Error analysis shows that typical delay contributors are captured logically and have intuitive effects on the predictions. It is found that the largest under-predictions are mainly driven by weather forecast errors and unexplained tactical ATC interventions. The results indicate that quantile-based transit time forecasts can provide airlines with a more risk-aware basis for fuel and schedule planning than fixed deterministic buffers. However, the relatively low R2 shows that a substantial share of the variation in FIR duration remains unexplained, largely associated with tactical ATC interventions that occur under otherwise acceptable weather and capacity conditions.
...
In the approach phase of a flight, aircraft transitions from the en-route phase to the final approach. The transit time between these phases is highly uncertain and frequently subject to delays. Efficient and reliable prediction of this time is essential for airline fuel planning and flight scheduling, yet current practice still relies largely on fixed deterministic buffers. Most existing work on arrival delay prediction focuses on deterministic models and aggregate indicators (e.g the difference between planned and actual arrival times), often at forecast horizons during the airborne phase. This paper develops and validates an explainable probabilistic forecasting model for flight duration within the Amsterdam Schiphol (AMS) Flight Information Region (FIR), with a forecast moment in the pre-departure phase. The primary objective is to forecast the duration within the AMS FIR using information available at planning while providing interpretable contributors of delay that can be clearly communicated to flight dispatchers and pilots. The study uses a historical operational dataset of roughly 280,000 inbound flights, combining airline planning data, AMS traffic data, and METAR/TAF weather reports. On the held-out test set, the model achieves a reduced MAE of 33% and a reduced RMSE of 26% relative to the current operational baseline, while capturing about one third of the variance in FIR duration (R2= 0.33). Error analysis shows that typical delay contributors are captured logically and have intuitive effects on the predictions. It is found that the largest under-predictions are mainly driven by weather forecast errors and unexplained tactical ATC interventions. The results indicate that quantile-based transit time forecasts can provide airlines with a more risk-aware basis for fuel and schedule planning than fixed deterministic buffers. However, the relatively low R2 shows that a substantial share of the variation in FIR duration remains unexplained, largely associated with tactical ATC interventions that occur under otherwise acceptable weather and capacity conditions.
Radiotelephony (RT) remains the primary medium for pilot-controller communication, yet extracting structured information from spoken exchanges is challenging. Deep learning approaches often depend on large annotated datasets, limiting use in data-scarce environments. This study evaluates open-source large language models (LLMs) for Structured Information Extraction (SIE) from ATC communications, with applications in assisting or automating pseudo-pilot tasks. We evaluate Llama 3.3 (70B) with baseline prompting and Gemma-3 (4B) with baseline and fine-tuned variants on ~500 utterances from NLR's ATM simulator. Performance is assessed on human transcripts and ASR outputs from Whisper models, with varying prompt contexts. Cross-sector generalization is tested across two ATC sectors. Using manual scoring, Llama 3.3 achieves micro-F1 0.95 on human transcripts and 0.86 on fine-tuned Whisper outputs. While Gemma-3 performed weaker in its baseline form, fine-tuning on a small sample led to notable improvements. Results demonstrate the potential of LLMs for ATC applications without the need for large annotated datasets.
...
Radiotelephony (RT) remains the primary medium for pilot-controller communication, yet extracting structured information from spoken exchanges is challenging. Deep learning approaches often depend on large annotated datasets, limiting use in data-scarce environments. This study evaluates open-source large language models (LLMs) for Structured Information Extraction (SIE) from ATC communications, with applications in assisting or automating pseudo-pilot tasks. We evaluate Llama 3.3 (70B) with baseline prompting and Gemma-3 (4B) with baseline and fine-tuned variants on ~500 utterances from NLR's ATM simulator. Performance is assessed on human transcripts and ASR outputs from Whisper models, with varying prompt contexts. Cross-sector generalization is tested across two ATC sectors. Using manual scoring, Llama 3.3 achieves micro-F1 0.95 on human transcripts and 0.86 on fine-tuned Whisper outputs. While Gemma-3 performed weaker in its baseline form, fine-tuning on a small sample led to notable improvements. Results demonstrate the potential of LLMs for ATC applications without the need for large annotated datasets.
As with many aspects of modern life, wind nowcasting and forecasting are integral parts of aviation and Air Traffic Management (ATM). This study investigates the use of a Denoising Diffusion Probabilistic Model (DDPM) for nowcasting (inpainting) and forecasting (image-to-video) of wind fields using aircraft-derived meteorological data. The DDPM, implemented with a U-Net backbone, demonstrated strong performance in nowcasting tasks, outperforming previous models such as the Lagrangian transportation-based Meteo-Particle (MP) model and
Physically Inspired Neural Network (PINN) approach with a 29% improvement in magnitude error and a 62% reduction in directional error. The nowcasting model achieved a magnitude
error of 2.03 m/s and a directional error of 4.2°, based on 190 test samples from late 2024. A key contribution lies in the DDPMs ability to produce more consistent and lower-variance predictions than prior methods. The RMSE improved on the PINN results by 29%, to 3.99 m/s. Despite these successes, forecasting proved significantly more challenging, with no meaningful results achieved. The study used ECMWF CERRA reanalysis data for training
and evaluated model performance with simulated aircraft tracks on known wind fields and with real aircraft-derived data from the The Royal Netherlands Meteorological Institute (KNMI)’s EMADDC dataset split into model input and validation subsets. High computational demands restricted testing capabilities, and uncertainty quantification and severe weather conditions remain challenging for the model. ...
Physically Inspired Neural Network (PINN) approach with a 29% improvement in magnitude error and a 62% reduction in directional error. The nowcasting model achieved a magnitude
error of 2.03 m/s and a directional error of 4.2°, based on 190 test samples from late 2024. A key contribution lies in the DDPMs ability to produce more consistent and lower-variance predictions than prior methods. The RMSE improved on the PINN results by 29%, to 3.99 m/s. Despite these successes, forecasting proved significantly more challenging, with no meaningful results achieved. The study used ECMWF CERRA reanalysis data for training
and evaluated model performance with simulated aircraft tracks on known wind fields and with real aircraft-derived data from the The Royal Netherlands Meteorological Institute (KNMI)’s EMADDC dataset split into model input and validation subsets. High computational demands restricted testing capabilities, and uncertainty quantification and severe weather conditions remain challenging for the model. ...
As with many aspects of modern life, wind nowcasting and forecasting are integral parts of aviation and Air Traffic Management (ATM). This study investigates the use of a Denoising Diffusion Probabilistic Model (DDPM) for nowcasting (inpainting) and forecasting (image-to-video) of wind fields using aircraft-derived meteorological data. The DDPM, implemented with a U-Net backbone, demonstrated strong performance in nowcasting tasks, outperforming previous models such as the Lagrangian transportation-based Meteo-Particle (MP) model and
Physically Inspired Neural Network (PINN) approach with a 29% improvement in magnitude error and a 62% reduction in directional error. The nowcasting model achieved a magnitude
error of 2.03 m/s and a directional error of 4.2°, based on 190 test samples from late 2024. A key contribution lies in the DDPMs ability to produce more consistent and lower-variance predictions than prior methods. The RMSE improved on the PINN results by 29%, to 3.99 m/s. Despite these successes, forecasting proved significantly more challenging, with no meaningful results achieved. The study used ECMWF CERRA reanalysis data for training
and evaluated model performance with simulated aircraft tracks on known wind fields and with real aircraft-derived data from the The Royal Netherlands Meteorological Institute (KNMI)’s EMADDC dataset split into model input and validation subsets. High computational demands restricted testing capabilities, and uncertainty quantification and severe weather conditions remain challenging for the model.
Physically Inspired Neural Network (PINN) approach with a 29% improvement in magnitude error and a 62% reduction in directional error. The nowcasting model achieved a magnitude
error of 2.03 m/s and a directional error of 4.2°, based on 190 test samples from late 2024. A key contribution lies in the DDPMs ability to produce more consistent and lower-variance predictions than prior methods. The RMSE improved on the PINN results by 29%, to 3.99 m/s. Despite these successes, forecasting proved significantly more challenging, with no meaningful results achieved. The study used ECMWF CERRA reanalysis data for training
and evaluated model performance with simulated aircraft tracks on known wind fields and with real aircraft-derived data from the The Royal Netherlands Meteorological Institute (KNMI)’s EMADDC dataset split into model input and validation subsets. High computational demands restricted testing capabilities, and uncertainty quantification and severe weather conditions remain challenging for the model.
Aviation contributes to global warming not only through its $\text{CO}_2$ emissions, but also through contrail formation. The latter may be combatted by taking this effect into account in flight trajectory optimization. Research already shows some possibilities of doing this, but often lacks integration with the operational limits that airlines need to adhere to. In this paper, an approach is described to first optimize flight trajectories for persistent contrail minimization and then show the feasibility of these trajectories as standard flight plans. This is done by using the open source OpenAP.top trajectory optimizer and atmospheric data. An entire day of European flights in and out of Schiphol are analyzed in determining the potential reduction of climate impact for these flights. It was demonstrated that the amount of persistent contrails can be reduced with optimized trajectories and it was shown that these trajectories can, to a large extent, be approximated by waypoints that are included in standard flight plans.
...
Aviation contributes to global warming not only through its $\text{CO}_2$ emissions, but also through contrail formation. The latter may be combatted by taking this effect into account in flight trajectory optimization. Research already shows some possibilities of doing this, but often lacks integration with the operational limits that airlines need to adhere to. In this paper, an approach is described to first optimize flight trajectories for persistent contrail minimization and then show the feasibility of these trajectories as standard flight plans. This is done by using the open source OpenAP.top trajectory optimizer and atmospheric data. An entire day of European flights in and out of Schiphol are analyzed in determining the potential reduction of climate impact for these flights. It was demonstrated that the amount of persistent contrails can be reduced with optimized trajectories and it was shown that these trajectories can, to a large extent, be approximated by waypoints that are included in standard flight plans.
Master thesis
(2025)
-
I. Apahidean, J. Sun, Mihai-Adelin Cîrsticǎ, Goran Pavlović, M.J. Ribeiro, P. Proesmans
This research tackles optimization measures applied to the European ATM Network. It focuses on the Network Manager’s (NM) ability to become an active decision-making entity, strengthening its current mediation position in the relationship with relevant stakeholders (air navigation service providers - ANSPs, airspace users - AUs) inside the Collaborative Decision-Making (CDM) process. A game theoretic model, with the aim to measure the acceptance of the stakeholders to partially cede decision autonomy to the NM within the aforementioned process with the goal of improving overall ATM Network performance, is investigated and analysed.
...
This research tackles optimization measures applied to the European ATM Network. It focuses on the Network Manager’s (NM) ability to become an active decision-making entity, strengthening its current mediation position in the relationship with relevant stakeholders (air navigation service providers - ANSPs, airspace users - AUs) inside the Collaborative Decision-Making (CDM) process. A game theoretic model, with the aim to measure the acceptance of the stakeholders to partially cede decision autonomy to the NM within the aforementioned process with the goal of improving overall ATM Network performance, is investigated and analysed.
Trajectory Optimisation for Contrail Reduction of Hydrogen-Powered Aircraft
Investigating the effect of hydrogen combustion technologies in aviation on trajectory optimisation strategies to reduce contrail climate impact
Airlines worldwide have committed to the goal of achieving carbon neutrality in 2050. This goal is to be reached using a combination of sustainable aviation fuel (SAF), carbon offsetting, operational improvements, and the implementation of new propulsion technologies. Electric propulsion is expected to be applied for short-haul flights, while hydrogen-powered aircraft have the potential for medium-range routes too. Although these aircraft types are certain to cut the in-flight carbon emissions of the aviation sector if they are integrated into commercial fleets, the impact of hydrogen-powered aircraft on non-CO2 climate effects is still rather unknown. The non-CO2 effects of conventional aircraft have been researched extensively, focussing primarily on the climate impact of NOX and contrails. Both climate effects are location dependent, meaning that the climate impact is correlated with local values of atmospheric properties. As the location dependency of contrails is the most significant, the bulk of the research on this topic is focused on trajectory optimisation for contrail reduction. The introduction of hydrogen-powered aircraft in the aviation system could add an extra dimension to this research, as the lack of carbon emissions reduce the negative effects of route diversions.
This research considers three aircraft concepts: a current aircraft powered by Jet A-1, an aircraft with technological improvements powered by a 70% SAF blend, and an aircraft powered by hydrogen combustion. The effect of trajectory optimisation for each aircraft type highly depends on the contrail climate impact of the fuel-optimal flight. Simulations in pycontrails show that flying on hydrogen can cause a decrease in contrail ice crystal numbers of 89% compared the contrail of a conventional aircraft, and 81% of that of future aircraft powered by a 70% SAF blend. The resulting decrease in climate impact is 87% and 79% compared to the two other aircraft types, respectively.
Two approaches to using contrail analysis for trajectory optimisation are proposed in this research. The first is based on using pycontrails to find correlations between the contrail climate impact and atmospheric properties, which can be used for formulate new algorithmic climate change functions (aCCFs). This method proved not to be applicable as low correlations were found between atmospheric variables at the location of contrail formation and contrail energy forcing. The physical processes modelled in pycontrails do not seem to sufficiently well-captured by aCCFs that only take into account the conditions at the contrail onset. The second method is based on directly forming a contrail cost grid in pycontrails. Although this method is more complex to implement, it yields more accurate results.
The resulting contrail climate impact grids are subsequently used for trajectory optimisation, performed in OpenAP-TOP. The fuel-optimal route per concept aircraft is determined for six different trajectories, after which weighted combinations of fuel and climate costs are passed through the program to end up with a range of small and larger route diversions for climate cost savings. The results show a high impact of trajectory optimisation for the Jet A-1 aircraft concept and the future aircraft concept flown on the 70% SAF blend, decreasing the P-ATR20 climate impact by approximately 25 − 50 pK and 10− 30 pK, respectively, for the analysed case studies. Route diversions for hydrogen-powered aircraft show limited benefits (< 3 pK), which can be directly related to the relatively small contribution of contrails to the total climate impact of hydrogen-powered flights. For their fuel-optimal routes though, hydrogenpowered
aircraft reduce the total climate impact of a flight greatly: generally, a 80-90% reduction in
total climate impact is realised compared to Jet A-1. ...
This research considers three aircraft concepts: a current aircraft powered by Jet A-1, an aircraft with technological improvements powered by a 70% SAF blend, and an aircraft powered by hydrogen combustion. The effect of trajectory optimisation for each aircraft type highly depends on the contrail climate impact of the fuel-optimal flight. Simulations in pycontrails show that flying on hydrogen can cause a decrease in contrail ice crystal numbers of 89% compared the contrail of a conventional aircraft, and 81% of that of future aircraft powered by a 70% SAF blend. The resulting decrease in climate impact is 87% and 79% compared to the two other aircraft types, respectively.
Two approaches to using contrail analysis for trajectory optimisation are proposed in this research. The first is based on using pycontrails to find correlations between the contrail climate impact and atmospheric properties, which can be used for formulate new algorithmic climate change functions (aCCFs). This method proved not to be applicable as low correlations were found between atmospheric variables at the location of contrail formation and contrail energy forcing. The physical processes modelled in pycontrails do not seem to sufficiently well-captured by aCCFs that only take into account the conditions at the contrail onset. The second method is based on directly forming a contrail cost grid in pycontrails. Although this method is more complex to implement, it yields more accurate results.
The resulting contrail climate impact grids are subsequently used for trajectory optimisation, performed in OpenAP-TOP. The fuel-optimal route per concept aircraft is determined for six different trajectories, after which weighted combinations of fuel and climate costs are passed through the program to end up with a range of small and larger route diversions for climate cost savings. The results show a high impact of trajectory optimisation for the Jet A-1 aircraft concept and the future aircraft concept flown on the 70% SAF blend, decreasing the P-ATR20 climate impact by approximately 25 − 50 pK and 10− 30 pK, respectively, for the analysed case studies. Route diversions for hydrogen-powered aircraft show limited benefits (< 3 pK), which can be directly related to the relatively small contribution of contrails to the total climate impact of hydrogen-powered flights. For their fuel-optimal routes though, hydrogenpowered
aircraft reduce the total climate impact of a flight greatly: generally, a 80-90% reduction in
total climate impact is realised compared to Jet A-1. ...
Airlines worldwide have committed to the goal of achieving carbon neutrality in 2050. This goal is to be reached using a combination of sustainable aviation fuel (SAF), carbon offsetting, operational improvements, and the implementation of new propulsion technologies. Electric propulsion is expected to be applied for short-haul flights, while hydrogen-powered aircraft have the potential for medium-range routes too. Although these aircraft types are certain to cut the in-flight carbon emissions of the aviation sector if they are integrated into commercial fleets, the impact of hydrogen-powered aircraft on non-CO2 climate effects is still rather unknown. The non-CO2 effects of conventional aircraft have been researched extensively, focussing primarily on the climate impact of NOX and contrails. Both climate effects are location dependent, meaning that the climate impact is correlated with local values of atmospheric properties. As the location dependency of contrails is the most significant, the bulk of the research on this topic is focused on trajectory optimisation for contrail reduction. The introduction of hydrogen-powered aircraft in the aviation system could add an extra dimension to this research, as the lack of carbon emissions reduce the negative effects of route diversions.
This research considers three aircraft concepts: a current aircraft powered by Jet A-1, an aircraft with technological improvements powered by a 70% SAF blend, and an aircraft powered by hydrogen combustion. The effect of trajectory optimisation for each aircraft type highly depends on the contrail climate impact of the fuel-optimal flight. Simulations in pycontrails show that flying on hydrogen can cause a decrease in contrail ice crystal numbers of 89% compared the contrail of a conventional aircraft, and 81% of that of future aircraft powered by a 70% SAF blend. The resulting decrease in climate impact is 87% and 79% compared to the two other aircraft types, respectively.
Two approaches to using contrail analysis for trajectory optimisation are proposed in this research. The first is based on using pycontrails to find correlations between the contrail climate impact and atmospheric properties, which can be used for formulate new algorithmic climate change functions (aCCFs). This method proved not to be applicable as low correlations were found between atmospheric variables at the location of contrail formation and contrail energy forcing. The physical processes modelled in pycontrails do not seem to sufficiently well-captured by aCCFs that only take into account the conditions at the contrail onset. The second method is based on directly forming a contrail cost grid in pycontrails. Although this method is more complex to implement, it yields more accurate results.
The resulting contrail climate impact grids are subsequently used for trajectory optimisation, performed in OpenAP-TOP. The fuel-optimal route per concept aircraft is determined for six different trajectories, after which weighted combinations of fuel and climate costs are passed through the program to end up with a range of small and larger route diversions for climate cost savings. The results show a high impact of trajectory optimisation for the Jet A-1 aircraft concept and the future aircraft concept flown on the 70% SAF blend, decreasing the P-ATR20 climate impact by approximately 25 − 50 pK and 10− 30 pK, respectively, for the analysed case studies. Route diversions for hydrogen-powered aircraft show limited benefits (< 3 pK), which can be directly related to the relatively small contribution of contrails to the total climate impact of hydrogen-powered flights. For their fuel-optimal routes though, hydrogenpowered
aircraft reduce the total climate impact of a flight greatly: generally, a 80-90% reduction in
total climate impact is realised compared to Jet A-1.
This research considers three aircraft concepts: a current aircraft powered by Jet A-1, an aircraft with technological improvements powered by a 70% SAF blend, and an aircraft powered by hydrogen combustion. The effect of trajectory optimisation for each aircraft type highly depends on the contrail climate impact of the fuel-optimal flight. Simulations in pycontrails show that flying on hydrogen can cause a decrease in contrail ice crystal numbers of 89% compared the contrail of a conventional aircraft, and 81% of that of future aircraft powered by a 70% SAF blend. The resulting decrease in climate impact is 87% and 79% compared to the two other aircraft types, respectively.
Two approaches to using contrail analysis for trajectory optimisation are proposed in this research. The first is based on using pycontrails to find correlations between the contrail climate impact and atmospheric properties, which can be used for formulate new algorithmic climate change functions (aCCFs). This method proved not to be applicable as low correlations were found between atmospheric variables at the location of contrail formation and contrail energy forcing. The physical processes modelled in pycontrails do not seem to sufficiently well-captured by aCCFs that only take into account the conditions at the contrail onset. The second method is based on directly forming a contrail cost grid in pycontrails. Although this method is more complex to implement, it yields more accurate results.
The resulting contrail climate impact grids are subsequently used for trajectory optimisation, performed in OpenAP-TOP. The fuel-optimal route per concept aircraft is determined for six different trajectories, after which weighted combinations of fuel and climate costs are passed through the program to end up with a range of small and larger route diversions for climate cost savings. The results show a high impact of trajectory optimisation for the Jet A-1 aircraft concept and the future aircraft concept flown on the 70% SAF blend, decreasing the P-ATR20 climate impact by approximately 25 − 50 pK and 10− 30 pK, respectively, for the analysed case studies. Route diversions for hydrogen-powered aircraft show limited benefits (< 3 pK), which can be directly related to the relatively small contribution of contrails to the total climate impact of hydrogen-powered flights. For their fuel-optimal routes though, hydrogenpowered
aircraft reduce the total climate impact of a flight greatly: generally, a 80-90% reduction in
total climate impact is realised compared to Jet A-1.
Improvements in air traffic management (ATM) operations offer a faster pathway to reducing aviation emissions compared to long-term technological solutions. Existing ATM performance frameworks rely on proxy indicators that fail to account for wind effects and vertical profile optimization. This research developed a fuel-based flight inefficiency quantification framework utilizing the open-source OpenAP aircraft performance model and OpenAP.top trajectory optimizer, incorporating wind effects and individual initial mass estimation. The methodology was applied to over 200,000 commercial operations at Amsterdam Airport Schiphol in 2024, comparing executed trajectories against wind-optimal references at city-pair and Flight Information Region (FIR) levels. Results show that strategic inefficiency consistently represents the larger component (7-25% for most routes), while tactical inefficiency is predominantly negative, indicating execution improvements through ATC interventions and wind exploitation. FIR analysis reveals distinct phase characteristics: horizontal inefficiency accounts for approximately 90% of climb inefficiency, while descent operations show vertical inefficiency contributing the larger share with greater seasonal variation in horizontal components. Total relative inefficiencies differ between phases (climb: 9-14%; descent: 24-30%), with the magnitude difference amplified by lower baseline fuel consumption during descent. Wind integration proved essential; for an example flight, omitting wind nearly doubled city-pair inefficiency while underestimating descent inefficiency. Initial mass estimation uncertainty propagates through the analysis and reported values represent conservative upper bounds with actual relative total inefficiencies potentially 15-30% lower. The framework provides researchers and air navigation service providers with tools for environmental performance monitoring and quantitative baselines for evaluating ATM initiatives.
...
Improvements in air traffic management (ATM) operations offer a faster pathway to reducing aviation emissions compared to long-term technological solutions. Existing ATM performance frameworks rely on proxy indicators that fail to account for wind effects and vertical profile optimization. This research developed a fuel-based flight inefficiency quantification framework utilizing the open-source OpenAP aircraft performance model and OpenAP.top trajectory optimizer, incorporating wind effects and individual initial mass estimation. The methodology was applied to over 200,000 commercial operations at Amsterdam Airport Schiphol in 2024, comparing executed trajectories against wind-optimal references at city-pair and Flight Information Region (FIR) levels. Results show that strategic inefficiency consistently represents the larger component (7-25% for most routes), while tactical inefficiency is predominantly negative, indicating execution improvements through ATC interventions and wind exploitation. FIR analysis reveals distinct phase characteristics: horizontal inefficiency accounts for approximately 90% of climb inefficiency, while descent operations show vertical inefficiency contributing the larger share with greater seasonal variation in horizontal components. Total relative inefficiencies differ between phases (climb: 9-14%; descent: 24-30%), with the magnitude difference amplified by lower baseline fuel consumption during descent. Wind integration proved essential; for an example flight, omitting wind nearly doubled city-pair inefficiency while underestimating descent inefficiency. Initial mass estimation uncertainty propagates through the analysis and reported values represent conservative upper bounds with actual relative total inefficiencies potentially 15-30% lower. The framework provides researchers and air navigation service providers with tools for environmental performance monitoring and quantitative baselines for evaluating ATM initiatives.
The advent and rise of low-cost, high-frequency short-haul flights in Europe increasingly necessitates the need of more sustainable travel methods for trips with a distance under 1000 km. Many different proposals and policies have been put in place, including full flight bans for trips under 500 km, but the research space still has not embraced a possible co-existence of major air and ground transport options in this segment.
This research addresses this by developing a reproducible method using openly accessible data to assess air and rail network capacities of three of the busiest air transport routes in Europe. A capacity analysis is conducted and the modelling of travel time, travel cost, change in carbon dioxide emissions and passenger experience is performed, in order to investigate the feasibility and logistics of shifting passengers between air and rail.
The study finds that 30-50% of passengers could shift completely to trains, given sufficient rail network capacity, significantly reducing total carbon dioxide emissions and improving passenger experience throughout. ...
This research addresses this by developing a reproducible method using openly accessible data to assess air and rail network capacities of three of the busiest air transport routes in Europe. A capacity analysis is conducted and the modelling of travel time, travel cost, change in carbon dioxide emissions and passenger experience is performed, in order to investigate the feasibility and logistics of shifting passengers between air and rail.
The study finds that 30-50% of passengers could shift completely to trains, given sufficient rail network capacity, significantly reducing total carbon dioxide emissions and improving passenger experience throughout. ...
The advent and rise of low-cost, high-frequency short-haul flights in Europe increasingly necessitates the need of more sustainable travel methods for trips with a distance under 1000 km. Many different proposals and policies have been put in place, including full flight bans for trips under 500 km, but the research space still has not embraced a possible co-existence of major air and ground transport options in this segment.
This research addresses this by developing a reproducible method using openly accessible data to assess air and rail network capacities of three of the busiest air transport routes in Europe. A capacity analysis is conducted and the modelling of travel time, travel cost, change in carbon dioxide emissions and passenger experience is performed, in order to investigate the feasibility and logistics of shifting passengers between air and rail.
The study finds that 30-50% of passengers could shift completely to trains, given sufficient rail network capacity, significantly reducing total carbon dioxide emissions and improving passenger experience throughout.
This research addresses this by developing a reproducible method using openly accessible data to assess air and rail network capacities of three of the busiest air transport routes in Europe. A capacity analysis is conducted and the modelling of travel time, travel cost, change in carbon dioxide emissions and passenger experience is performed, in order to investigate the feasibility and logistics of shifting passengers between air and rail.
The study finds that 30-50% of passengers could shift completely to trains, given sufficient rail network capacity, significantly reducing total carbon dioxide emissions and improving passenger experience throughout.
Master thesis
(2024)
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A.I. Gheorghe, M.J. Ribeiro, J. Sun, Pascal Hop, Benjamin Cramet, J.M. Hoekstra, R. Merino Martinez
Predicting aircraft Take-Off Weight (TOW) has been a long-sought task by aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Unfortunately, TOW being a sensitive parameter to operational trends and cost indices, aircraft operators tend to keep it confidential. In recent years, Machine Learning (ML) algorithms have achieved increased prediction accuracy and capabilities in the field, provided the availability of TOW data. This paper studies the implementation of gradient boosting algorithms as well as Random Forests to better understand which algorithm is best-suited for aircraft TOW prediction (prior to take-off) solely based on Flight PLan (FPL) and Terminal Aerodrome Forecast (TAF) parameters. The study focused on flights at Amsterdam Airport Schiphol (AMS) for training the algorithms, using an 80-20% train-test split. Between Gradient Boosting Decision Trees (GBDTs), LightGBM, XGBoost, and Random Forests, GBDTs achieved the smallest Mean Absolute Percentage Error (MAPE) with 1.71 and 2.17% on the training and testing datasets, respectively. The most influencing feature proved to be the requested cruise speed, followed by great circle distance between airports, and aircraft type. The model was validated on Paris - Charles de Gaulle Airport (CDG) and Brussels South Charleroi Airport (CRL), proving its independence from airport type. However, the distribution of flights in the training dataset, especially that of aircraft and airline types, proved to be an influencing factor for the model's applicability to other airports. Future work includes expanding the training dataset to all flights in the European network, and introducing trajectory-based features such as aircraft speed intent. With a larger training dataset, neural network algorithms could also be explored. Finally, regarding the improvement of trajectory predictions, it was found that better accuracy of TOW predictions does not suffice and that other operational parameters' effect should be investigated, especially speed profiles.
...
Predicting aircraft Take-Off Weight (TOW) has been a long-sought task by aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Unfortunately, TOW being a sensitive parameter to operational trends and cost indices, aircraft operators tend to keep it confidential. In recent years, Machine Learning (ML) algorithms have achieved increased prediction accuracy and capabilities in the field, provided the availability of TOW data. This paper studies the implementation of gradient boosting algorithms as well as Random Forests to better understand which algorithm is best-suited for aircraft TOW prediction (prior to take-off) solely based on Flight PLan (FPL) and Terminal Aerodrome Forecast (TAF) parameters. The study focused on flights at Amsterdam Airport Schiphol (AMS) for training the algorithms, using an 80-20% train-test split. Between Gradient Boosting Decision Trees (GBDTs), LightGBM, XGBoost, and Random Forests, GBDTs achieved the smallest Mean Absolute Percentage Error (MAPE) with 1.71 and 2.17% on the training and testing datasets, respectively. The most influencing feature proved to be the requested cruise speed, followed by great circle distance between airports, and aircraft type. The model was validated on Paris - Charles de Gaulle Airport (CDG) and Brussels South Charleroi Airport (CRL), proving its independence from airport type. However, the distribution of flights in the training dataset, especially that of aircraft and airline types, proved to be an influencing factor for the model's applicability to other airports. Future work includes expanding the training dataset to all flights in the European network, and introducing trajectory-based features such as aircraft speed intent. With a larger training dataset, neural network algorithms could also be explored. Finally, regarding the improvement of trajectory predictions, it was found that better accuracy of TOW predictions does not suffice and that other operational parameters' effect should be investigated, especially speed profiles.
To verify the aircraft position provided by Automatic Dependent Surveillance-Broadcast (ADS-B)
transponders, multilateration (MLAT) technique incorporates time difference of arrival (TDOA) measurements at multiple ground-based receivers to estimate the corresponding distances between those and the aircraft. This approach requires precise time synchronization among receivers that can not always be guaranteed. Alternatively, received signal strength (RSS) measurements can be utilized to derive these distances. In this paper, crowdsourced RSS measurements from 43 receivers were used to construct parameterized signal propagation models that capture the relationship between RSS and distance. The quality of these models
was evaluated by examination of model parameter and estimated distance errors in both 2D and 3D. The results show that at most 26.3% of available RSS measurements could be represented by the models given the cut-off criteria for model parameter errors. Moreover, the models with higher parameter errors demonstrated poor ability to capture RSS measurements at greater distances. The localization errors in MLAT with TDOA were compared to MLAT with RSS where the later resulted in more accurate position estimation in cases where the receiver clocks were not synchronized. However, MLAT with TDOA generally produced significantly more accurate position estimation given the reliable timestamps of signal arrival. The assessment of localization accuracy using crowdsourced data resulted in root mean square errors of 118.1 meters in MLAT with TDOA and 9858.6 meters in MLAT with RSS in 2D, representing the best results obtained. ...
transponders, multilateration (MLAT) technique incorporates time difference of arrival (TDOA) measurements at multiple ground-based receivers to estimate the corresponding distances between those and the aircraft. This approach requires precise time synchronization among receivers that can not always be guaranteed. Alternatively, received signal strength (RSS) measurements can be utilized to derive these distances. In this paper, crowdsourced RSS measurements from 43 receivers were used to construct parameterized signal propagation models that capture the relationship between RSS and distance. The quality of these models
was evaluated by examination of model parameter and estimated distance errors in both 2D and 3D. The results show that at most 26.3% of available RSS measurements could be represented by the models given the cut-off criteria for model parameter errors. Moreover, the models with higher parameter errors demonstrated poor ability to capture RSS measurements at greater distances. The localization errors in MLAT with TDOA were compared to MLAT with RSS where the later resulted in more accurate position estimation in cases where the receiver clocks were not synchronized. However, MLAT with TDOA generally produced significantly more accurate position estimation given the reliable timestamps of signal arrival. The assessment of localization accuracy using crowdsourced data resulted in root mean square errors of 118.1 meters in MLAT with TDOA and 9858.6 meters in MLAT with RSS in 2D, representing the best results obtained. ...
To verify the aircraft position provided by Automatic Dependent Surveillance-Broadcast (ADS-B)
transponders, multilateration (MLAT) technique incorporates time difference of arrival (TDOA) measurements at multiple ground-based receivers to estimate the corresponding distances between those and the aircraft. This approach requires precise time synchronization among receivers that can not always be guaranteed. Alternatively, received signal strength (RSS) measurements can be utilized to derive these distances. In this paper, crowdsourced RSS measurements from 43 receivers were used to construct parameterized signal propagation models that capture the relationship between RSS and distance. The quality of these models
was evaluated by examination of model parameter and estimated distance errors in both 2D and 3D. The results show that at most 26.3% of available RSS measurements could be represented by the models given the cut-off criteria for model parameter errors. Moreover, the models with higher parameter errors demonstrated poor ability to capture RSS measurements at greater distances. The localization errors in MLAT with TDOA were compared to MLAT with RSS where the later resulted in more accurate position estimation in cases where the receiver clocks were not synchronized. However, MLAT with TDOA generally produced significantly more accurate position estimation given the reliable timestamps of signal arrival. The assessment of localization accuracy using crowdsourced data resulted in root mean square errors of 118.1 meters in MLAT with TDOA and 9858.6 meters in MLAT with RSS in 2D, representing the best results obtained.
transponders, multilateration (MLAT) technique incorporates time difference of arrival (TDOA) measurements at multiple ground-based receivers to estimate the corresponding distances between those and the aircraft. This approach requires precise time synchronization among receivers that can not always be guaranteed. Alternatively, received signal strength (RSS) measurements can be utilized to derive these distances. In this paper, crowdsourced RSS measurements from 43 receivers were used to construct parameterized signal propagation models that capture the relationship between RSS and distance. The quality of these models
was evaluated by examination of model parameter and estimated distance errors in both 2D and 3D. The results show that at most 26.3% of available RSS measurements could be represented by the models given the cut-off criteria for model parameter errors. Moreover, the models with higher parameter errors demonstrated poor ability to capture RSS measurements at greater distances. The localization errors in MLAT with TDOA were compared to MLAT with RSS where the later resulted in more accurate position estimation in cases where the receiver clocks were not synchronized. However, MLAT with TDOA generally produced significantly more accurate position estimation given the reliable timestamps of signal arrival. The assessment of localization accuracy using crowdsourced data resulted in root mean square errors of 118.1 meters in MLAT with TDOA and 9858.6 meters in MLAT with RSS in 2D, representing the best results obtained.
On Understanding Environmental Inefficiencies in Air Traffic Management
A Causal Inference Approach
Master thesis
(2024)
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J.N. Aalders, I.C. Dedoussi, J. Sun, F. Domingos de Azevedo Quadros, M. Snellen
Addressing the increasingly urgent need for sustainable aviation solutions, this study explores operational innovations as a quicker and more scalable addition to novel zero-emission propulsion systems. Through the use of regression-based causal inference methods, this study aims to understand the relationship between flight fuelburn inefficiency and the factors causing these inefficiencies. Such an approach allows for the attribution of inefficiencies to factors on an overall scale, requiring less specific domain knowledge for initial results. A case study, involving a sample of 100,000 flights, representative of European operations, reveals that airspace structure (3.2% increase in inefficiency) and turbulence along the flight plan (2.5% increase) are the leading causes, while variations in average airspeed, congestion, and crosswind contribute the least to flight inefficiency. A compilation of the results shows that the performed analysis leaves 61% of the observed flight inefficiency unaccounted for. Future work would include the exploration of different metrics even closer to actual climate and air quality effects, as well as detailed uncertainty quantification. The developed flight inefficiency prediction model allows experimentation with counterfactual scenarios, contributing to the global transition towards more sustainable air transport networks.
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Addressing the increasingly urgent need for sustainable aviation solutions, this study explores operational innovations as a quicker and more scalable addition to novel zero-emission propulsion systems. Through the use of regression-based causal inference methods, this study aims to understand the relationship between flight fuelburn inefficiency and the factors causing these inefficiencies. Such an approach allows for the attribution of inefficiencies to factors on an overall scale, requiring less specific domain knowledge for initial results. A case study, involving a sample of 100,000 flights, representative of European operations, reveals that airspace structure (3.2% increase in inefficiency) and turbulence along the flight plan (2.5% increase) are the leading causes, while variations in average airspeed, congestion, and crosswind contribute the least to flight inefficiency. A compilation of the results shows that the performed analysis leaves 61% of the observed flight inefficiency unaccounted for. Future work would include the exploration of different metrics even closer to actual climate and air quality effects, as well as detailed uncertainty quantification. The developed flight inefficiency prediction model allows experimentation with counterfactual scenarios, contributing to the global transition towards more sustainable air transport networks.
Air traffic delays have a major impact on the aviation industry, affecting airlines, passengers, and the broader ecosystem. With increasing regulatory and sustainability pressures, accurate delay predictions are now critical, as they enable reductions in contingency and discretionary fuel on flights, lowering total fuel usage. This research aims to develop an explainable supervised learning model to improve existing en route delay predictions, focusing on intercontinental flights from North America to Amsterdam Schiphol Airport. While prior studies have explored flight delay prediction, they have not addressed two critical research gaps identified in this research: the inclusion of day-of-operations features, such as passenger information, aircraft weights, and cost index, and the use of transatlantic flight data for
predictions 90 minutes before departure. To address these gaps, two Gradient-Boosted models, CatBoost and LightGBM, were trained using internal airline, airport, and METAR data. Both models outperformed the airline’s current in-use statistical model, with CatBoost achieving an MAE of 3.44 minutes and RMSE of 4.61 minutes and LightGBM achieving an MAE of 3.43 minutes and RMSE of 4.56 minutes. However, the model’s overall predictive accuracy, as
indicated by the R2 scores, remained relatively low, reflecting the inherent challenges of en route delay forecasting. The most significant performance increase over the current model was observed under adverse weather conditions. Despite these improvements, the test run also showed that the performance of the models deteriorates quickly as significant
differences exist in TAFs and actual weather. An explainability framework was developed to aid end users of the model, providing insights into the model’s decision-making process and helping to build user confidence in its predictions. This research advances en route delay prediction by providing more accurate delay forecasts, particularly in critical weather conditions, and proposes practical improvements to support future studies focused on enhancing model adaptability across diverse operational contexts. ...
predictions 90 minutes before departure. To address these gaps, two Gradient-Boosted models, CatBoost and LightGBM, were trained using internal airline, airport, and METAR data. Both models outperformed the airline’s current in-use statistical model, with CatBoost achieving an MAE of 3.44 minutes and RMSE of 4.61 minutes and LightGBM achieving an MAE of 3.43 minutes and RMSE of 4.56 minutes. However, the model’s overall predictive accuracy, as
indicated by the R2 scores, remained relatively low, reflecting the inherent challenges of en route delay forecasting. The most significant performance increase over the current model was observed under adverse weather conditions. Despite these improvements, the test run also showed that the performance of the models deteriorates quickly as significant
differences exist in TAFs and actual weather. An explainability framework was developed to aid end users of the model, providing insights into the model’s decision-making process and helping to build user confidence in its predictions. This research advances en route delay prediction by providing more accurate delay forecasts, particularly in critical weather conditions, and proposes practical improvements to support future studies focused on enhancing model adaptability across diverse operational contexts. ...
Air traffic delays have a major impact on the aviation industry, affecting airlines, passengers, and the broader ecosystem. With increasing regulatory and sustainability pressures, accurate delay predictions are now critical, as they enable reductions in contingency and discretionary fuel on flights, lowering total fuel usage. This research aims to develop an explainable supervised learning model to improve existing en route delay predictions, focusing on intercontinental flights from North America to Amsterdam Schiphol Airport. While prior studies have explored flight delay prediction, they have not addressed two critical research gaps identified in this research: the inclusion of day-of-operations features, such as passenger information, aircraft weights, and cost index, and the use of transatlantic flight data for
predictions 90 minutes before departure. To address these gaps, two Gradient-Boosted models, CatBoost and LightGBM, were trained using internal airline, airport, and METAR data. Both models outperformed the airline’s current in-use statistical model, with CatBoost achieving an MAE of 3.44 minutes and RMSE of 4.61 minutes and LightGBM achieving an MAE of 3.43 minutes and RMSE of 4.56 minutes. However, the model’s overall predictive accuracy, as
indicated by the R2 scores, remained relatively low, reflecting the inherent challenges of en route delay forecasting. The most significant performance increase over the current model was observed under adverse weather conditions. Despite these improvements, the test run also showed that the performance of the models deteriorates quickly as significant
differences exist in TAFs and actual weather. An explainability framework was developed to aid end users of the model, providing insights into the model’s decision-making process and helping to build user confidence in its predictions. This research advances en route delay prediction by providing more accurate delay forecasts, particularly in critical weather conditions, and proposes practical improvements to support future studies focused on enhancing model adaptability across diverse operational contexts.
predictions 90 minutes before departure. To address these gaps, two Gradient-Boosted models, CatBoost and LightGBM, were trained using internal airline, airport, and METAR data. Both models outperformed the airline’s current in-use statistical model, with CatBoost achieving an MAE of 3.44 minutes and RMSE of 4.61 minutes and LightGBM achieving an MAE of 3.43 minutes and RMSE of 4.56 minutes. However, the model’s overall predictive accuracy, as
indicated by the R2 scores, remained relatively low, reflecting the inherent challenges of en route delay forecasting. The most significant performance increase over the current model was observed under adverse weather conditions. Despite these improvements, the test run also showed that the performance of the models deteriorates quickly as significant
differences exist in TAFs and actual weather. An explainability framework was developed to aid end users of the model, providing insights into the model’s decision-making process and helping to build user confidence in its predictions. This research advances en route delay prediction by providing more accurate delay forecasts, particularly in critical weather conditions, and proposes practical improvements to support future studies focused on enhancing model adaptability across diverse operational contexts.
Automatic Control With Human-Like Reasoning
Exploring Language Model Embodied Air Traffic Agents
Recent developments in language models have created new opportunities in air traffic control studies. The current focus is primarily on text and language-based use cases. However, these language models may offer a higher potential impact in the air traffic control domain, thanks to their ability to interact with air traffic environments in an embodied agent form. They also provide a language-like reasoning capability to explain their decisions, which has been a significant roadblock for the implementation of automatic air traffic control. This paper investigates the application of a language model-based agent with function-calling and learning capabilities to resolve air traffic conflicts without human intervention. The main components of this research are foundational large language models, tools that allow the agent to interact with the simulator, and a new concept, the experience library. An innovative part of this research, the experience library, is a vector database that stores synthesized knowledge that agents have learned from interactions with the simulations and language models. To evaluate the performance of our language model-based agent, both open-source and closed-source models were tested. The results of our study reveal significant differences in performance across various configurations of the language model-based agents. The best-performing configuration was able to solve almost all 120 but one imminent conflict scenario, including up to four aircraft at the same time. Most importantly, the agents are able to provide human-level text explanations on traffic situations and conflict resolution strategies.
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Recent developments in language models have created new opportunities in air traffic control studies. The current focus is primarily on text and language-based use cases. However, these language models may offer a higher potential impact in the air traffic control domain, thanks to their ability to interact with air traffic environments in an embodied agent form. They also provide a language-like reasoning capability to explain their decisions, which has been a significant roadblock for the implementation of automatic air traffic control. This paper investigates the application of a language model-based agent with function-calling and learning capabilities to resolve air traffic conflicts without human intervention. The main components of this research are foundational large language models, tools that allow the agent to interact with the simulator, and a new concept, the experience library. An innovative part of this research, the experience library, is a vector database that stores synthesized knowledge that agents have learned from interactions with the simulations and language models. To evaluate the performance of our language model-based agent, both open-source and closed-source models were tested. The results of our study reveal significant differences in performance across various configurations of the language model-based agents. The best-performing configuration was able to solve almost all 120 but one imminent conflict scenario, including up to four aircraft at the same time. Most importantly, the agents are able to provide human-level text explanations on traffic situations and conflict resolution strategies.
The emissions of the transport sector inside the EU-27 have risen by 33 % between 1990 and 2019. A modal shift from unsustainable transport towards more environmentally friendly transport modes can be taken as one solution to mitigate the overall emission of the transport sector. In this paper, multiple open-source models and databases are utilized to compare travel emissions and time of air travel and various ground transport options, including car, bus, and rail. Compared with previous research that relies on closed-sourced or hand-collected data to extract public ground transportation routes information, this paper utilizes the openly accessible General Transit Feed Specification(GTFS) database, facilitating the calculation at a large scale inside EU-27. 820 pairs of routes between popular 41 city centers inside EU-27 are selected for comparison. The results consistently demonstrate that air travel always produces higher emissions per passenger than rail and bus travel for all routes. Emissions from cars are significantly influenced by occupancy rates and the type of vehicle fuel. The emissions from a single person in a petrol/diesel car can exceed those from air travel. However, if four people travel in a hybrid electric or electric vehicle, the per-passenger emission can be similar to rail. Among all public transport, the rail is the most competitive one to replace air travel by offering passengers similar travel time and reducing emissions. The trade-off factor between emissions and time is also investigated on its effect on the passenger route choice decision. In addition, this paper offers insights into the development of emission models and provides recommendations for various stakeholders.
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The emissions of the transport sector inside the EU-27 have risen by 33 % between 1990 and 2019. A modal shift from unsustainable transport towards more environmentally friendly transport modes can be taken as one solution to mitigate the overall emission of the transport sector. In this paper, multiple open-source models and databases are utilized to compare travel emissions and time of air travel and various ground transport options, including car, bus, and rail. Compared with previous research that relies on closed-sourced or hand-collected data to extract public ground transportation routes information, this paper utilizes the openly accessible General Transit Feed Specification(GTFS) database, facilitating the calculation at a large scale inside EU-27. 820 pairs of routes between popular 41 city centers inside EU-27 are selected for comparison. The results consistently demonstrate that air travel always produces higher emissions per passenger than rail and bus travel for all routes. Emissions from cars are significantly influenced by occupancy rates and the type of vehicle fuel. The emissions from a single person in a petrol/diesel car can exceed those from air travel. However, if four people travel in a hybrid electric or electric vehicle, the per-passenger emission can be similar to rail. Among all public transport, the rail is the most competitive one to replace air travel by offering passengers similar travel time and reducing emissions. The trade-off factor between emissions and time is also investigated on its effect on the passenger route choice decision. In addition, this paper offers insights into the development of emission models and provides recommendations for various stakeholders.
Air Traffic Control (ATC) is tasked with ensuring safe separation between aircraft in a given Controlled Traffic Region (CTR). To achieve this an Air Traffic Controller (ATCo) verbally gives clearances using over the air communication. These clearances are kept track of by the ATCo using so-called ‘flight-strips’, which in modern systems are often digital. The allocation of an ATCo’s time is an important factor in the achievable traffic density within a CTA, which makes ATC an interesting domain to use Automatic Speech Recognition (ASR) models to allow a computer system to ‘listen in’ to the conversation of the ATCo. Although previous research has been done to create such models, few of these result in open available models or domain specific corpora for the creation of such a model. This study will therefore use two open in-domain and one out-of-domain corpora to create such a model and in this process identify domain specific challenges and how these challenges can, in certain cases, be mitigated.
...
Air Traffic Control (ATC) is tasked with ensuring safe separation between aircraft in a given Controlled Traffic Region (CTR). To achieve this an Air Traffic Controller (ATCo) verbally gives clearances using over the air communication. These clearances are kept track of by the ATCo using so-called ‘flight-strips’, which in modern systems are often digital. The allocation of an ATCo’s time is an important factor in the achievable traffic density within a CTA, which makes ATC an interesting domain to use Automatic Speech Recognition (ASR) models to allow a computer system to ‘listen in’ to the conversation of the ATCo. Although previous research has been done to create such models, few of these result in open available models or domain specific corpora for the creation of such a model. This study will therefore use two open in-domain and one out-of-domain corpora to create such a model and in this process identify domain specific challenges and how these challenges can, in certain cases, be mitigated.
Passenger transportation in Europe is often duplicated using modes of transportation which are environmentally inefficient. Quantifying the carbon dioxide emission inefficiencies of flights versus transit is beneficial to understand the potential savings of a modal shift. In this paper, we analyze the emissions in Europe from multi-stop flights using flight data from March 2019. The excess emissions are quantified by comparing each multi-stop flight with an intermodal journey that does not exceed 60 minutes of extra travel time. We find that on average, transfer passengers using intermodality can reduce their journey’s total
(segment) well-to-wheel and life-cycle assessment emissions by 33% (80%) and 30% (72%), respectively. 840 thousand (19 % of total) transfer passengers starting or ending their journey in Europe can skip the feeder flight while saving an average of 28 minutes of door-to-door travel time. For air travellers taking intra-European multi-stop flights, 157 thousand transfer passengers (10% of the total) do not have to even enter an airport. Further insights regarding the European mobility vision are made, with recommendations for various stakeholders. ...
(segment) well-to-wheel and life-cycle assessment emissions by 33% (80%) and 30% (72%), respectively. 840 thousand (19 % of total) transfer passengers starting or ending their journey in Europe can skip the feeder flight while saving an average of 28 minutes of door-to-door travel time. For air travellers taking intra-European multi-stop flights, 157 thousand transfer passengers (10% of the total) do not have to even enter an airport. Further insights regarding the European mobility vision are made, with recommendations for various stakeholders. ...
Passenger transportation in Europe is often duplicated using modes of transportation which are environmentally inefficient. Quantifying the carbon dioxide emission inefficiencies of flights versus transit is beneficial to understand the potential savings of a modal shift. In this paper, we analyze the emissions in Europe from multi-stop flights using flight data from March 2019. The excess emissions are quantified by comparing each multi-stop flight with an intermodal journey that does not exceed 60 minutes of extra travel time. We find that on average, transfer passengers using intermodality can reduce their journey’s total
(segment) well-to-wheel and life-cycle assessment emissions by 33% (80%) and 30% (72%), respectively. 840 thousand (19 % of total) transfer passengers starting or ending their journey in Europe can skip the feeder flight while saving an average of 28 minutes of door-to-door travel time. For air travellers taking intra-European multi-stop flights, 157 thousand transfer passengers (10% of the total) do not have to even enter an airport. Further insights regarding the European mobility vision are made, with recommendations for various stakeholders.
(segment) well-to-wheel and life-cycle assessment emissions by 33% (80%) and 30% (72%), respectively. 840 thousand (19 % of total) transfer passengers starting or ending their journey in Europe can skip the feeder flight while saving an average of 28 minutes of door-to-door travel time. For air travellers taking intra-European multi-stop flights, 157 thousand transfer passengers (10% of the total) do not have to even enter an airport. Further insights regarding the European mobility vision are made, with recommendations for various stakeholders.
Estimating wind fields using drones in a network
Estimating hyperlocal wind fields with on-board sensors on quadcopters
Charting hyperlocal wind using a drone is a challenge of increased attention as it unlocks potential in a variety of fields. In context of the METeo Sensors In the Sky project, this study proposes a method to estimate the magnitude and direction of wind using a quadcopter in hover and cruise without a dedicated wind sensor. Only on-board sensors are used, with no knowledge of thrust and rpm. A deterministic method models drag experienced by the drone classically as a quadratic function of true airspeed, and estimates wind by deducting the estimated true airspeed with the GPS ground speed. Additionally, a particle filter is implemented and compared to the deterministic method. To validate the proposed methods, a series of verification flights is conducted in which the drone is flown straight into the wind, perpendicular to, and away from the wind. The results show that the proposed method can estimate wind for various ground speeds and altitudes. The root mean square error ranges between 0.3-2.0 m/s and 5-35 degrees in most scenarios with high true airspeeds. In most cases, the particle filter shows a slight improvement over the deterministic method, at the cost of reduced adaptivity to wind changes (gusts).
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Charting hyperlocal wind using a drone is a challenge of increased attention as it unlocks potential in a variety of fields. In context of the METeo Sensors In the Sky project, this study proposes a method to estimate the magnitude and direction of wind using a quadcopter in hover and cruise without a dedicated wind sensor. Only on-board sensors are used, with no knowledge of thrust and rpm. A deterministic method models drag experienced by the drone classically as a quadratic function of true airspeed, and estimates wind by deducting the estimated true airspeed with the GPS ground speed. Additionally, a particle filter is implemented and compared to the deterministic method. To validate the proposed methods, a series of verification flights is conducted in which the drone is flown straight into the wind, perpendicular to, and away from the wind. The results show that the proposed method can estimate wind for various ground speeds and altitudes. The root mean square error ranges between 0.3-2.0 m/s and 5-35 degrees in most scenarios with high true airspeeds. In most cases, the particle filter shows a slight improvement over the deterministic method, at the cost of reduced adaptivity to wind changes (gusts).
Punctuality is a key performance indicator for any airline. Hub-and-spoke airlines are particularly committed to on-time arrivals to guarantee passenger connections. Flights that are delayed at departure need to compensate for the lost time whilst airborne. Because fueling takes place well before scheduled departure, predicted departure delays determine the planned fuel amounts for en-route speed optimization. To prevent unnecessary fuel burn, airlines benefit from highly accurate departure delay predictions. This study aims to extend previous work on airline departure delay forecasting to a dynamic and probabilistic domain, whilst incorporating novel day-of-operations airline information to further minimize prediction errors. Random Forest, CatBoost, and Deep Neural Network models were proposed for a case study on KLM departures from Amsterdam Airport Schiphol between 1 January 2020 and 1 August 2023. The Random Forest model was selected for its superior probabilistic performance and high accuracy in predicting delays between 5 and 25 minutes, for which en-route speed optimization has the largest effect. The departure delay probability distribution forecasts are made at six distinct prediction moments: 90, 75, 60, 45, 30, and 15 minutes before scheduled departure time. At the 90-minute prediction horizon, the model reaches a Mean Absolute Error (MAE) of 8.46 minutes and a Root Mean Square Error (RMSE) of 11.91 minutes. Simultaneously, for 76% of flights, the actual delay is within the predicted probability distribution range. Considering the costs and emissions associated with the decision-making following the departure delay prediction model, this study puts strong emphasis on explainability. Flight dispatchers are therefore provided not only the predicted departure delay but also the main factors impacting the prediction, explaining the context of the flight. The versatility of the model was demonstrated in two shadow runs, where delays caused by familiar and unfamiliar factors were successfully predicted.
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Punctuality is a key performance indicator for any airline. Hub-and-spoke airlines are particularly committed to on-time arrivals to guarantee passenger connections. Flights that are delayed at departure need to compensate for the lost time whilst airborne. Because fueling takes place well before scheduled departure, predicted departure delays determine the planned fuel amounts for en-route speed optimization. To prevent unnecessary fuel burn, airlines benefit from highly accurate departure delay predictions. This study aims to extend previous work on airline departure delay forecasting to a dynamic and probabilistic domain, whilst incorporating novel day-of-operations airline information to further minimize prediction errors. Random Forest, CatBoost, and Deep Neural Network models were proposed for a case study on KLM departures from Amsterdam Airport Schiphol between 1 January 2020 and 1 August 2023. The Random Forest model was selected for its superior probabilistic performance and high accuracy in predicting delays between 5 and 25 minutes, for which en-route speed optimization has the largest effect. The departure delay probability distribution forecasts are made at six distinct prediction moments: 90, 75, 60, 45, 30, and 15 minutes before scheduled departure time. At the 90-minute prediction horizon, the model reaches a Mean Absolute Error (MAE) of 8.46 minutes and a Root Mean Square Error (RMSE) of 11.91 minutes. Simultaneously, for 76% of flights, the actual delay is within the predicted probability distribution range. Considering the costs and emissions associated with the decision-making following the departure delay prediction model, this study puts strong emphasis on explainability. Flight dispatchers are therefore provided not only the predicted departure delay but also the main factors impacting the prediction, explaining the context of the flight. The versatility of the model was demonstrated in two shadow runs, where delays caused by familiar and unfamiliar factors were successfully predicted.