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Dart presentation 4

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Data-Driven Aircraft Trajectory Prediction Research, Data Analytics for ATM, ATM Complexity, DCB problems, SESAR 2020

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Dart presentation 4

  1. 1. DART. Data-driven AiRcraft Trajectory prediction research José Manuel Cordero García jmcordero@e-crida.enaire.es Data Driven ATM: Going Digital! 8th March 2018
  2. 2. DART Consortium SESAR 2020 - Exploratory Research 2 University of Piraeus Research Center - UPRC Reference Centre for Research, Development and Innovation in ATM (CRIDA) Fraunhofer Gesellschaft zur Förderung der Angewandten Forschung e.V. (IAIS) Boeing Research & Technology Europe S.L.U. (BR&T-E)
  3. 3. DART Objectives and Impact SESAR 2020 - Exploratory Research 3 Data Driven Trajectory Prediction Improved Network Management Improved airport operations Collaborative decision making Challenge of exploring the applicability of data science and complexity science techniques to the ATM domain with the aim of improving aircraft trajectory prediction capabilities of the ATM system It is expected that data-driven techniques help to improve the accuracy of predictions by complementing classical model-based prediction approaches.
  4. 4. Problem addressed by DART SESAR 2020 - Exploratory Research 4 Aircraft Operators ANSPs Network Manager Minimizing the cost throught maximizing the adherence to the airlines preferred FPs. Decide which flights to modify to resolve sector imbalances and potential conflicts. Minimizing the sector imbalances and potential conflicts. DART aims to develop a collaborative decision making process that would support multi-objective optimization taking into account stakeholders’ expectations.
  5. 5. CTP Collaborative Trajectory Prediction Imbalances and conflicts detection (ANSP) AOspreferredFPs Iterative Optimization process Select the flights to modify (NM) STP Single Trajectory Prediction Trajectory predictions from an individual trajectory perspective STP predict new single trajectories for these flights CTP detection of imbalances and conflicts, selection of flight to modify and proposition of new FPs Optimaltrajectories AOs propose new FP for these flights Network Manager Air Navigation Service Providers Solution proposed: Single and Collaborative Trajectory Prediction SESAR 2020 - Exploratory Research 5 DART contributions Collaborative Trajectory Prediction Individual Trajectory Prediction Aircraft Operators
  6. 6. DART Concept SESAR 2020 - Exploratory Research 6 Data-driven algorithm for single trajectory predictions: - Hidden Markov Models (HMM) - Similarity-based retrieval (HMM/clustering) - Reinforcement Learning Models Collaborative reinforcement learning algorithms for agent based modelling of ATM Visual Interfaces for Interactive exploration of solutions and decision making DART explores the applicability of a collection of machine learning and agent- based models and algorithms to derive a data-driven trajectory prediction capability. Advanced visual analytics techniques will be used to facilitate data exploration, quality assessment, and algorithms parameters and features selection.
  7. 7. DART Data Sources SESAR 2020 - Exploratory Research 7 DARTSurveillance Data Weather Data: NOAA forecasts, SIGMET, TAF Flight Plans DDR -2 Spanish Operational Data Airspace Structure DDR-2 Spanish Operational Data Reconstructed Trajectories BR&T-E Trajectory Predictor using Flight Plans Aircraft Intent Descriptions BR&T-E The datasets used for training and testing the algorithms are a key component of the project.
  8. 8. Visually supported Trajectory Prediction SESAR 2020 - Exploratory Research 8 Visually supported detection of clusters of trajectories and flight plans from Madrid to Barcelona Historical data provide recurrent patterns of trajectories (enriched with contextual information – e.g. weather) that data- driven methods learn. Prediction of a single trajectory involves choosing the pattern that fits better a flight plan w.r.t. contextual data.
  9. 9. DART Expected Achievements & Conclusions Expected Achievements (Maturity: TRL-1) • Data-driven algorithms for single trajectory predictions; Implementation and evaluation of the following algorithms: (a) Hidden Markov Models (HMM), (b) Similarity-based retrieval (HMM/clustering), (c) Reinforcement Learning Models applied on Aircraft Intent. • Collaborative reinforcement learning algorithms for agent based modelling of ATM; Agents correspond to aircraft in specific trajectories. Implementation and evaluation of collaborative reinforcement learning algorithms • Visual Interfaces for Interactive exploration of solutions and decision making, providing an overview of modeling results in space and time. SESAR 2020 - Exploratory Research 9
  10. 10. This project has received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No [number] The opinions expressed herein reflect the author’s view only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein. Thank you DART. Data-driven AiRcraft Trajectory prediction research
  11. 11. Collaborative Trajectory Prediction • Reinforced Learning approach considered (work in progress): 1. Independent Reinforcement Learners approach Each agent (flight) is self-interested and learns by itself to resolve the DCB problem, by measuring its own reward after each Decision 2. Edge-Based Collaborative Reinforcement Learners approach Reward received from global state and joint action of all agents (global optimum) 3. Agent-based Collaborative Reinforcement Learners approach Variant of the previous approach. Agents are not flights, but flights+sectors. Would allow rerouting extension 11International Workshop on Uncertainty and Air Traffic Management
  12. 12. Planificación de Trayectorias 4D de Aeronaves en Entornos Multiobjetivo Esther Calvo Fernández 12 Selección del número de individuos de la población inicial Optimal planning expectations bucle 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 %mejoraencombustible 0 2 4 6 8 bucle 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 %mejoraendesequilibrios 0 10 20 30 40 bucle 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 %mejoraencombustible 0 2 4 6 8 bucle 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 %mejoraendesequilibrios 0 10 20 30 40 bucle 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 %mejoraencombustible 0 2 4 6 8 bucle 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 %mejoraendesequilibrios 0 10 20 30 40 Improvement in fuel consumption Improvements in number of hotspots 200 individuals 100 individuals 50 individuals
  13. 13. Collaborative Trajectory Prediction 13 • Early results (Method 2, Sparse Collaborative Q-Learning – Agent- based decomposition – Edge based update): International Workshop on Uncertainty and Air Traffic Management

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