Using ADS-B big data pattern analysis to improve the quality of multivariate 4D trajectory optimization strategies
Final Report Abstract
Efficient air transport operations rely heavily on optimizing aircraft trajectories both individually and systemwide. Optimization aims primarily to minimize fuel consumption, thereby reducing operational costs and environmental impact, while also aiming to cut flight time and costs to improve operational efficiency and passenger satisfaction. Powerful trajectory optimization additionally balances airspace utilization, allowing for better management of single flights and flows and reduced traffic congestion. A pivotal surveillance technology in modern air traffic management (ATM) is ADS-B (Automatic Dependent Surveillance-Broadcast), which enables aircraft to broadcast position and other trajectory data. This system has dramatically increased the public availability of flight track data compared to legacy radar systems, useful for validating aircraft performance models and optimization techniques. On the other hand, managing the resulting massive volumes of ADS-B data poses a significant computational challenge, with over 200,000 messages per second worldwide. The present research project has leveraged big ADS-B data analysis to enhance the layout of ATM structures, airline networks, routing structures, and fleet scheduling behaviors. This initiative aims to support both individual and system-wide trajectory optimization by assessing efficiency gaps in the global ATM system. We developed a novel metric to assess the horizontal flight efficiency (HFE), addressing the limitations of existing metrics not accounting for meteorological aspects. The project’s analysis revealed significant differences in routing structures between Europe and China. Prohibited areas were a major factor causing deviations from the shortest route in China, whereas atmospheric conditions, network requirements, divers aircraft types, and flight planning procedures had a similar but minor impact on flight efficiency during the cruise phase in both regions. Multi-criteria trajectory optimization tackling the various emission types considering weather, operational constraints, and prohibited/restricted airspace areas showed that flown ground distances could be significantly reduced, demonstrating fuel saving potential. Further analysis focused on vertical flight efficiency (VFE), identifying criteria for optimizing emission efficient flight profiles. Different optimization strategies were tested, highlighting the greater impact of weather conditions on fuel consumption compared to aerodynamic factors. The method showed potential fuel savings of nearly 13% in China and 10+% in Europe. The research also integrated dynamic cost indexing (CI), i.e. adopting the aircraft’s operating speed in cruise along the flight following time constraints, and re-routing within an airline schedule recovery model to mitigate the effects of arrival delays. This model, focusing on turnaround optimization at hub airports, showed that dynamic CI and re-routing can improve operational efficiency and minimize costs. Also, a concept called "Long Range Air Traffic Flow Management" (LR-ATFM), aiming to control aircraft arrival at lead times rates of several flight hours to prevent over-demand in the approach sector of large airports with a high rate of long-haul flights, typical for the Asian region, e.g., Singapore Changi airport: Speed adjustments and route changes applied to dedicated aircraft were found the most effective control measures when implemented several hours in advance. Additionally, the authors emphasized accurate prediction of landing times for ATM applications, e.g., to enhance operational efficiency: Machine learning (ML) methods were used to predict crucial flight progress milestones, achieving high accuracy. Given the significant impact of meteorological conditions on trajectory prediction, various interpolation techniques often scarce three-dimensional weather data were evaluated. These methods lead to high-resolution meteorological information used to improve accuracy and efficiency of trajectory prediction. Lastly, the project considered delay propagation prediction in an air traffic network using Granger causality. This method helped understanding the interdependencies between airports, revealing differences in delay propagation between Europe and China and assisting in predicting and managing delays in air traffic operations.
Publications
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Using Open Source Data for Landing Time Prediction with Machine Learning Methods. 8th OpenSky Symposium 2020, 5. MDPI.
Chen, Gong; Rosenow, Judith; Schultz, Michael & Okhrin, Ostap
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Impact of Chinese and European Airspace Constraints on Trajectory Optimization. Aerospace, 8(11), 338.
Rosenow, Judith; Chen, Gong; Fricke, Hartmut; Sun, Xiaoqian & Wang, Yanjun
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Airline schedule recovery at hub airports including dynamic cost indexing and re-routing. In International Conference on Research in Air Transportation (ICRAT 2022), 2022, Tampa, Florida.
Jan Evler, Judith Rosenow & Hartmut Fricke
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Factors Impacting Chinese and European Vertical Fight Efficiency. Aerospace, 9(2), 76.
Rosenow, Judith; Chen, Gong; Fricke, Hartmut & Wang, Yanjun
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Importance of Weather Conditions in a Flight Corridor. Stats, 5(1), 312-338.
Chen, Gong; Fricke, Hartmut; Okhrin, Ostap & Rosenow, Judith
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Long Range Air Traffic Flow Management with Flight-Specific Flight Performance. Future Transportation, 2(2), 310-327.
Rosenow, Judith; Asadi, Ehsan; Lubig, Daniel; Schultz, Michael & Fricke, Hartmut
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Arrival time management in real weather conditions. In SESAR Innovation Days (SID 2023), 2023, Sevilla, Spain.
Judith Rosenow, Daniel Lubig & Hartmut Fricke
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Bayesian inference of aircraft operating speeds for stochastic medium-term trajectory prediction. In 42nd Digital Avionics Systems Conference (DASC 2023), 2023, Barcelona, Spain.
Thomas Zeh, Judith Rosenow & Hartmut Fricke
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Flight delay propagation inference in air transport networks using the multilayer perceptron. Journal of Air Transport Management, 114, 102510.
Chen, Gong; Fricke, Hartmut; Okhrin, Ostap & Rosenow, Judith
