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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
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bucle
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
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bucle
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75
%mejoraencombustible
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Improvement in fuel consumption Improvements in number of hotspots
200
individuals
100
individuals
50
individuals

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