Air Traffic Management R&D Seminar - 13th USA/EUROPE - ATM Seminar
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EUROCONTROL
13th USA/EUROPE
Air Traffic Management R&D Seminar
17 - 21 June 2019
Vienna, Austria
Contents
Seminar at-a-glance 5
Full programme 9
Abstracts 21
Keynote speakers and panellists 43
3
4
Seminar at-a-glance
5Monday 17 June 2019
18:30 Early Registration, welcome cocktail
Tuesday 18 June 2019
07:00 Registration
Plenary opening session - Grand Waterfront Hall
08:00 Welcome and Logistics
Dirk Schaefer (EUROCONTROL, European chair) & Eric Neiderman (FAA, US Chair)
08:15 Airlines‘ Perspective on Air Traffic Management
Alexis von Hoensbroech (CEO Austrian Airlines)
08:45 Urban Air Mobility - Closer Than You Think
Eric Mueller (Uber Elevate)
09:30 Is CPDLC Communication Secure and can Identity-Defined Networking help it?
Andrei Gurtov (Linköping University)
09:50 Coffee
Track 1: Grand Waterfront Hall 1 Track 2: Grand Waterfront Hall 2 Track 3: Grand Waterfront Hall 3
TRAJECTORY MANAGEMENT SEPARATION INTEGRATED AIRPORT/AIRSIDE
Session Chair: Shannon Zelinski (NASA) Session Chair: Georges Mykoniatis OPERATIONS
(ENAC) Session Chair:Hartmut Fricke (TUDresden)
10:15 40 Sgorcea 63 Ritchie 51 Jung
11:00 43 Zeghal 16 Z. Wang 84 Rodríguez-Sanz
11:45 25 Evans 18 Pham 92 Balakrishnan
12:30 Lunch
SAFETY SEPARATION INTEGRATED AIRPORT/AIRSIDE
Session Chair: Mark Hansen Session Chair: Hamsa Balakrishnan OPERATIONS
(UC Berkeley) (MIT) Session Chair: Midori Tanino (FAA)
14:00 75 Rabiller/Fricke 2 Fleming 57 R. Wang
14:45 95 L. Dai 56 T. Polishchuk 80 Ali
15:30 Coffee
SAFETY COMPLEXITY NETWORK AND STRATEGIC FLOW
Session Chair: Tatjana Bolić SessionChair:GeorgTrausmuth(Frequentis) MANAGEMENT
(University of Trieste) Session Chair: Nicolas Durand (ENAC)
16:15 28 Metz 3 Helmke 15 Ruiz
17:00 44 Stroeve 106 Tarakan 105 Andreeva-Mori
Wednesday 19 June 2019
06:00 5K fun run
Track 1: Grand Waterfront Hall 1 Track 2: Grand Waterfront Hall 2 Track 3: Grand Waterfront Hall 3
UAS COMPLEXITY NETWORK AND STRATEGIC FLOW
Session Chair: Natesh Manikoth (FAA) Session Chair: Marc Bourgois MANAGEMENT
EUROCONTROL) SessionChair:JoseMiguelDePablo(ENAIRE)
08:30 73 Gheorghisor 107 Churchill 11 Ma
09:15 50 Consiglio 5 Olive 22 Taylor
10:00 65 S. Li 46 Deshmukh 29 Gianazza
10:45 Coffee
UAS ENVIRONMENT PERFORMANCE ANALYSIS AND METRICS
Session Chair: Billy Josefsson (LFV) Session Chair: Chris Dorbian (FAA) Session Chair: Michael Ball
(University of Maryland)
11:15 64 V. Polishchuk 49 Thomas 27 Coleman
12:00 77 Cho 86 Rosenow 41 Zeghal
12:45 30 V. Polishchuk 54 Dalmau 42 Zeghal
6 13:30 Light lunchThursday 20 June 2019
Track 1: Grand Waterfront Hall 1 Track 2: Grand Waterfront Hall 2 Track 3: Grand Waterfront Hall 3
SURVEILLANCE TRAJECTORY PREDICTION PERFORMANCE ANALYSIS AND METRICS
Session Chair: Joseph Post (FAA) Session Chair: Eric Hoffman Session Chair: Michael Ball
(EUROCONTROL) (University of Maryland)
08:30 1 Dautermann 87 Buelta 39 Pollock
09:15 67 Takeichi 21 Alligier 102 Balakrishnan
10:00 13 Bone 66 W. Dai 88 Prats
10:45 Coffee
SURVEILLANCE WEATHER SYSTEMS AND TOOLS TO
Session Chair: Dirk Kuegler (DLR) Session Chair: Craig Wanke (MITRE) IMPROVE ATM PERFORMANCE
Session Chair: Jacco Hoekstra (TU Delft)
11:15 19 Dean 53 Hayashi 62 Coupe
12:00 24 Howell 85 Seenivasan 70 Xu
12:45 Lunch
HUMAN FACTORS WEATHER SYSTEMS AND TOOLS TO
Session Chair: Sandy Lozito (NASA) Session Chair: Tom Reynolds (MIT LL) IMPROVE ATM PERFORMANCE
Session Chair: Jacco Hoekstra (TU Delft)
14:00 103 Meyer 78 Reitmann 83 Idris
14:45 90 Cooper 37 Steinheimer 93 Estes
15:30 Coffee
HUMAN FACTORS WEATHER COMPLEXITY
Session Chair: Miquel Àngel Piera (UAB) Session Chair: Mark Weber (NOAA) Session Chair: Dirk Schaefer
(EUROCONTROL)
16:00 58 Borst 12 Jones 59 Monmousseau
16:45 31 Ellerbroek 4 Valenzuela 97 Tereshchenko
19:00 Gala dinner
Friday 21 June 2019
PLENARY CLOSING SESSION - Grand Waterfront Hall
08:00 Perspectives from US and Europe
The US Perspective - Joseph Post (FAA)
The European Perspective - Paul Bosman (EUROCONTROL)
News from the SESAR Knowledge Transfer Network Engage - Andrew Cook (University of Westminster)
09:30 Coffee
10:00 Panel: Where will Urban Air Mobility be in 20 years? - Grand Waterfront Hall
Moderators: Sandy Lozito (NASA) and Munish Khurana (EUROCONTROL)
Participants: François Sillion (Uber Elevate), R. John Hansman (MIT), Natesh Manikoth (FAA), Parimal
Kopardekar (NASA), Joerg P. Mueller (Airbus)
11:30 Closing Session
Best Paper Awards including selected Best Paper Teaser Presentations
Announcement ICRAT 2020 and ICRAT Student Grand Challenge
Closing and Announcement ATM Seminar 2021 - Eric Neiderman (FAA) and Dirk Schaefer
(EUROCONTROL)
13:00 Lunch
Committee Meeting (working lunch) 7ATM 2019 Seminar
Full programme
9Monday 17 June 2019
18:30 Early registration and welcome cocktail
10Tuesday 18 June 2019
07:00 Registration
PLENARY OPENING SESSION - Grand Waterfront Hall
08:00 Welcome and Logistics
Dirk Schaefer (EUROCONTROL, European chair) & Eric Neiderman (FAA, US Chair)
08:15 Airlines‘ Perspective on Air Traffic Management
Alexis von Hoensbroech (CEO Austrian Airlines)
08:45 Urban Air Mobility - Closer Than You Think
Eric Mueller (Uber Elevate)
09:30 Is CPDLC Communication Secure and can Identity-Defined Networking help it?
Andrei Gurtov (Linköping University)
09:50 Coffee
Tuesday 18 June - Track 1: Grand Waterfront Hall 1
TRAJECTORY MANAGEMENT - Session Chair: Shannon Zelinski (NASA)
Time Paper Title Authors (presenter in bold)
10:15 40 Integrated Time-Based Management and Roland M. Sgorcea, Lesley A. Weitz, Ryan W.
Performance-Based Navigation Design for Huleatt (MITRE), Ian M. Levitt & Robert E. Mount
Trajectory-Based Operations (FAA)
11:00 43 Enroute Traffic Overflows versus Arrival Mana- Raphaël Christien, Eric Hoffman & Karim Zeghal
gement Delays (EUROCONTROL)
11:45 25 Using Machine-Learning to Dynamically Gene- Antony D. Evans (Crown Consulting) & Paul U.
rate Operationally Acceptable Strategic Reroute Lee (NASA)
Options
12:30 Lunch
SAFETY - Session Chair: Mark Hansen (UC Berkeley)
Time Paper Title Authors (presenter in bold)
14:00 75 Analysis of Safety Performances for Parallel Stanley Förster, Hartmut Fricke (TU Dresden),
Approach Operations with Performance-based Bruno Rabiller, Brian Hickling, Bruno Favennec &
Navigation Karim Zeghal (EUROCONTROL)
14:45 95 Modelling Go-Around Occurrence Lu Dai, Yulin Liu & Mark Hansen (UC Berkeley)
15:30 Coffee
SAFETY - Session Chair: Tatjana Bolić (University of Trieste)
Time Paper Title Authors (presenter in bold)
16:15 28 What is the Potential of a Bird Strike Advisory Isabel C. Metz (DLR & TU Delft), Thorsten
System? Mühlhausen (DLR), Joost Ellerbroek (TU Delft),
Dirk Kügler (DLR) & Jacco M. Hoekstra (TU Delft)
17:00 44 Development of a Collision Avoidance Valida- Sybert Stroeve, Henk Blom (NLR), Carlos
tion and Evaluation Tool (CAVEAT): Addressing Hernandez Medel, Carlos García Daroca, Alvaro
the Intrinsic Uncertainty in TCAS II and ACAS X Arroyo Cebeira (everis) & Stanislaw Drozdowski
(EUROCONTROL)
11Tuesday 18 June - Track 2: Grand Waterfront Hall 2
SEPARATION - Session Chair: Georges Mykoniatis (ENAC)
Time Paper Title Authors (presenter in bold)
10:15 63 EnAcT: Generating Aircraft Encounters using a James A. Ritchie III, Andrew J. Fabian (FAA),
Spherical Earth Model Nidhal C. Bouaynaya (Rowan University) & Mike
M. Paglione (FAA)
11:00 16 Learning Real Trajectory Data to Enhance Zhengyi Wang (ENAC), Man Liang (University
Conflict Detection Accuracy in Closest Point of of South Australia), Daniel Delahaye & Weilu Wu
Approach Problem (ENAC)
11:45 18 A Machine Learning Approach for Conflict Duc-Thinh Pham, Ngoc Phu Tran, Sameer Alam,
Resolution in Dense Traffic Scenarios with Vu Duong (Nanyang Technological University) &
Uncertainties Daniel Delahaye (ENAC)
12:30 Lunch
SEPARATION - Session Chair: Hamsa Balakrishnan (MIT)
Time Paper Title Authors (presenter in bold)
14:00 2 Guaranteed Conflict: When Speed Advisory Xiyuan Ge (University of Washington), Minghui
doesn’t Work for Time-based Flow Management Sun & Cody Fleming (University of Virginia)
14:45 56 Automation for Separation with CDOs: Dynamic Raúl Sáez, Xavier Prats (UPC), Tatiana
Aircraft Arrival Routes Polishchuk, Valentin Polishchuk & Christiane
Schmidt (Linköping University)
15:30 Coffee
COMPLEXITY - Session Chair: Georg Trausmuth (Frequentis)
Time Paper Title Authors (presenter in bold)
16:15 3 Cost Reductions enabled by Machine Learning Hartmut Helmke, Matthias Kleinert, Jürgen Rataj
in ATM (DLR), Petr Motlicek (Idiap), Christian Kern (Austro
Control), Dietrich Klakow (Saarland University) &
Petr Hlousek (Air Navigation Services of the Czech
Republic)
17:00 106 Characterizing National Airspace System Opera- Shuo Chen, Hunter Kopald, Rob Tarakan,
tions Using Automated Voice Data Processing Gaurish Anand & Karl Meyer (MITRE)
12Tuesday 18 June - Track 3: Grand Waterfront Hall 3
INTEGRATED AIRPORT/AIRSIDE OPERATIONS - Session Chair: Hartmut Fricke (TU Dresden)
Time Paper Title Authors (presenter in bold)
10:15 51 Field Evaluation of the Baseline Integrated Yoon C. Jung, William J. Coupe, Al Capps, Shawn
Arrival, Departure, and Surface Capabilities at Engelland & Shivanjli Sharma (NASA)
Charlotte Douglas International Airport
11:00 84 Assessment of the Airport Operational Álvaro Rodríguez-Sanz, José Manuel Cordero
Dynamics Using a Multistate System Approach (CRIDA), Beatriz Rubio Fernández, Fernando
Gómez Comendador & Rosa Arnaldo Valdés
(UPM)
11:45 92 A Comparative Analysis of Departure Metering Sandeep Badrinath, Hamsa Balakrishnan (MIT),
at Paris (CDG) and Charlotte (CLT) Airports Ji Ma & Daniel Delahaye (ENAC)
12:30 Lunch
INTEGRATED AIRPORT/AIRSIDE OPERATIONS - Session Chair: Midori Tanino (FAA)
Time Paper Title Authors (presenter in bold)
14:00 57 Departure Management with Robust Gate Ruixin Wang, Cyril Allignol, Nicolas Barnier &
Allocation Jean-Baptiste Gotteland (ENAC)
14:45 80 Impact of Stochastic Delays, Turnaround Time Hasnain Ali, Yash Guleria, Sameer Alam, Vu N.
and Connection Time on Missed Connections at Duong (Nanyang Technological University) &
Low Cost Airports Michael Schultz (DLR)
15:30 Coffee
NETWORK AND STRATEGIC FLOW MANAGEMENT - Session Chair: Nicolas Durand (ENAC)
Time Paper Title Authors (presenter in bold)
16:15 15 A Novel Air Traffic Flow Management Model to Sergio Ruiz, Hamid Kadour & Peter Choroba
Optimise the Network Delay (EUROCONTROL)
17:00 105 Operational Concept of Traffic Pattern Classifier Adriana Andreeva-Mori & Naoki Matayoshi
for Optimal Ground Holding (JAXA)
13Wednesday 19 June 2019
06:00 5K fun run
Wednesday 19 June - Track 1: Grand Waterfront Hall 1
UAS - Session Chair: Natesh Manikoth (FAA)
Time Paper Title Authors (presenter in bold)
08:30 73 Modelling and Simulation for Reliable LTE- Izabela Gheorghisor, Angela Chen, Leonid
based Communications in the National Airspace Globus, Timothy Luc & Phillip Schrader (MITRE)
System
09:15 50 Sense and Avoid Characterization of the Inde- Maria Consiglio (NASA), Brendan Duffy & Swee
pendent Configurable Architecture for Reliable Balachandran (National Institute of Aerospace),
Operations of Unmanned Systems Louis Glaab & César Muñoz (NASA)
10:00 65 Optimizing Collision Avoidance in Dense Sheng Li (Stanford University), Maxim Egorov
Airspace using Deep Reinforcement Learning (Airbus) & Mykel J. Kochenderfer (Stanford
University)
10:45 Coffee
UAS - Session Chair: Billy Josefsson (LFV)
Time Paper Title Authors (presenter in bold)
11:15 64 A Geometric Approach Towards Airspace Parker D. Vascik (MIT), Vishwanath Bulusu
Assessment for Emerging Operations (UC Berkeley), Jungwoo Cho (Korea Advanced
Institute of Science and Technology) & Valentin
Polishchuk (Linköping University)
12:00 77 Extraction and Interpretation of Geometrical Jungwoo Cho & Yoonjin Yoon (Korea Advanced
and Topological Properties of Urban Airspace Institute of Science and Technology)
for UAS Operations
12:45 30 Density-Adapting Layers towards PBN for UTM Vincent Duchamp (ENAC), Leonid Sedov &
Valentin Polishchuk (Linköping University)
13:30 Light lunch
14Wednesday 19 June - Track 2: Grand Waterfront Hall 2
COMPLEXITY - Session Chair: Marc Bourgois (EUROCONTROL)
Time Paper Title Authors (presenter in bold)
08:30 107 Clustering Aircraft Trajectories on the Airport Andrew Churchill & Michael Bloem (Mosaic)
Surface
09:15 5 Identifying Anomalies in past en-route Trajec- Xavier Olive & Luis Basora (ONERA)
tories with Clustering and Anomaly Detection
Methods
10:00 46 Data-Driven Precursor Detection Algorithm for Raj Deshmukh, Dawei Sun & Inseok Hwang
Terminal Airspace Operations (Purdue University)
10:45 Coffee
ENVIRONMENT - Session Chair: Chris Dorbian (FAA)
Time Paper Title Authors (presenter in bold)
11:15 49 Advanced Operational Procedure Design Con- Jacqueline Thomas, Alison Yu, Clement Li, Pedro
cepts for Noise Abatement Manuel Maddens Toscano & R. John Hansman
(MIT)
12:00 86 Condensation Trails in Trajectory Optimization Judith Rosenow & Hartmut Fricke (TU Dresden)
12:45 54 Using Wind Observations from Nearby Aircraft Ramon Dalmau, Xavier Prats (UPC) & Brian
to Update the Optimal Descent Trajectory in Baxley (NASA)
Real-time
13:30 Light lunch
15Wednesday 19 June - Track 3: Grand Waterfront Hall 3
NETWORK AND STRATEGIC FLOW MANAGEMENT - Session Chair: Jose Miguel De Pablo
(ENAIRE)
Time Paper Title Authors (presenter in bold)
08:30 11 Airway Network Flow Management using Qing Cai, Chunyao Ma, Sameer Alam, Vu N.
Braess’s Paradox Duong (Nanyang Technological University) &
Banavar Sridhar (NASA)
09:15 22 Strategic Flight Cancellation under Ground Christine Taylor, Shin-Lai Tien, Erik Vargo &
Delay Program Uncertainty Craig Wanke (MITRE)
10:00 29 Optimizing Successive Airspace Configurations David Gianazza (ENAC)
with a Sequential A* Algorithm
10:45 Coffee
PERFORMANCE ANALYSIS AND METRICS - Session Chair: Michael Ball (University of
Maryland)
Time Paper Title Authors (presenter in bold)
11:15 27 Statistical Model to Estimate the Benefit of Wake Nastaran Coleman, Dave Knorr & Almira Rama-
Turbulence Re-Categorization dani (FAA)
12:00 41 Spacing and Pressure to Characterise Arrival Raphaël Christien, Eric Hoffman & Karim Zeghal
Sequencing (EUROCONTROL)
12:45 42 Vertical Efficiency in Descent Compared to Best Pierrick Pasutto, Eric Hoffman & Karim Zeghal
Local Practices (EUROCONTROL)
13:30 Light lunch
16Thursday 20 June 2019
Thursday 20 June - Track 1: Grand Waterfront Hall 1
SURVEILLANCE - Session Chair: Joseph Post (FAA)
Time Paper Title Authors (presenter in bold)
08:30 1 GLS Approaches using SBAS: a SBAS to GBAS Thomas Dautermann, Thomas Ludwig, Robert
Converter Geister, Lutz Ehmke, Max Fermor (DLR), Matthew
Bruce & Markus Schwendener (Flight Calibration
Services)
09:15 67 Direct Modelling of Flight Time Uncertainty Noboru Takeichi & Taiki Yamada (Tokyo Metro-
as a Function of Flight Condition and Weather politan University)
Forecast
10:00 13 Air Traffic Controller use of Interval Manage- Randall Bone (MITRE)
ment during Terminal Area Metering
10:45 Coffee
SURVEILLANCE - Session Chair: Dirk Kuegler (DLR)
Time Paper Title Authors (presenter in bold)
11:15 19 Reduced Separation in US Oceanic Airspace Dan Howell, Rob Dean (Regulus) & Joseph Post
Benefits Analysis through Fast-Time Modelling (FAA)
12:00 24 Benefits and Costs of ADS-B In Efficiency Ap- Dan Howell, Rob Dean & Gary Paull (Regulus)
plications in US Airspace Fast-Time Modelling
Results and Preliminary Economic Analysis
12:45 Lunch
HUMAN FACTORS - Session Chair: Sandy Lozito (NASA)
Time Paper Title Authors (presenter in bold)
14:00 103 Validation of an Empiric Method for Safety Lothar Meyer, Maximilian Peukert, Billy
Assessment of Multi Remote Tower Josefsson (LFV) & Jonas Lundberg (Linköping
University)
14:45 90 Analysis of Long Duration Eye-Tracking Experi- Prithiviraj Muthumanickam, Aida Nordman
ments in a Remote Tower Environment (Linköping University), Supathida Boonsong
(LFV), Jonas Lundberg & Matthew Cooper
(Linköping University)
15:30 Coffee
HUMAN FACTORS - Session Chair: Miquel Àngel Piera (UAB)
Time Paper Title Authors (presenter in bold)
16:00 58 Solution Space Concept: Human-Machine Inter- Rolf Klomp, Rick Riegman, Clark Borst, Max
face for 4D Trajectory Management Mulder & René van Paassen (TU Delft)
16:45 31 Conformal Automation for Air Traffic Control Sjoerd van Rooijen, Joost Ellerbroek, Clark Borst
using Convolutional Neural Networks & Erik-Jan van Kampen (TU Delft)
19:00 Gala dinner
17Thursday 20 June - Track 2: Grand Waterfront Hall 2
TRAJECTORY PREDICTION - Session Chair: Eric Hoffman (EUROCONTROL)
Time Paper Title Authors (presenter in bold)
08:30 87 Iterative Learning Control for Precise Aircraft Almudena Buelta, Alberto Olivares & Ernesto
Trajectory Tracking in Continuous Climb Staffetti (Universidad Rey Juan Carlos)
Operations
09:15 21 Predictive Distribution of the Mass and Speed Richard Alligier (ENAC)
Profile to Improve Aircraft Climb Prediction
10:00 66 A Heuristic Algorithm for Aircraft 4D Trajectory Weibin Dai, Jun Zhang (National Key Lab of CNS/
Optimization Based on Bezier Curve ATM), Daniel Delahaye (ENAC) & Xiaoqian Sun
(National Key Lab of CNS/ATM)
10:45 Coffee
WEATHER - Session Chair: Craig Wanke (MITRE)
Time Paper Title Authors (presenter in bold)
11:15 53 Evaluation of a Dynamic Weather-Avoidance Re- Miwa Hayashi, Doug Isaacson & Huabin Tang
routing Tool in Adjacent-Center Arrival Metering (NASA)
12:00 85 Model Predictive Control Approach to Storm Dinesh B. Seenivasan, Alberto Olivares &
Avoidance for Multiple Aircraft Ernesto Staffetti (Universidad Rey Juan Carlos)
12:45 Lunch
WEATHER - Session Chair: Tom Reynolds (MIT LL)
Time Paper Title Authors (presenter in bold)
14:00 78 Advanced Quantification of Weather Impact on Stefan Reitmann (DLR), Sameer Alam (Nanyang
Air Traffic Management - Intelligent Weather Technological University) and Michael Schultz
Categorization with Machine Learning (DLR)
14:45 37 Quantification of Weather Impact on Arrival Martin Steinheimer, Christian Kern & Markus
Management Kerschbaum (Austro Control)
15:30 Coffee
WEATHER - Session Chair: Mark Weber (NOAA)
Time Paper Title Authors (presenter in bold)
16:00 12 Estimating Flow Rates in Convective Weather: A James C. Jones & Yan Glina (MIT Lincoln Lab)
Simulation-Based Approach
16:45 4 An Approach to En-Route Sector Demand Pre- Alfonso Valenzuela, Antonio Franco, Damián
diction subject to Thunderstorms Rivas (University of Seville), Daniel Sacher &
Jürgen Lang (MeteoSolutions)
19:00 Gala dinner
18Thursday 20 June - Track 3: Grand Waterfront Hall 3
PERFORMANCE ANALYSIS AND METRICS - Session Chair: Michael Ball (University of
Maryland)
Time Paper Title Authors (presenter in bold)
08:30 39 Time-Based Delivery Accuracy Requirements Matthew R. Pollock, Lesley A. Weitz, Jared A.
for Achieving Performance Based Navigation Hicks & John M. Timberlake (MITRE)
Objectives
09:15 102 A Spectral Approach towards Analyzing Air Max Z. Li, Karthik Gopalakrishnan, Hamsa
Traffic Network Disruptions Balakrishnan (MIT) & Kristyn Pantoja (Texas
A&M University)
10:00 88 Identifying the Sources of Flight Inefficiency Xavier Prats, Ramon Dalmau & Cristina Barrado
from Historical Aircraft Trajectories (UPC)
10:45 Coffee
SYSTEMS AND TOOLS TO IMPROVE ATM PERFORMANCE - Session Chair: Jacco Hoekstra
(TU Delft)
Time Paper Title Authors (presenter in bold)
11:15 62 Scheduling Improvements Following the Phase William J. Coupe, Hanbong Lee, Yoon Jung
1 Field Evaluation of the ATD-2 Integrated Ar- (NASA), Liang Chen (Moffett Technologies) &
rival, Departure, and Surface Concept Isaac Robeson (Mosaic)
12:00 70 Stochastic Tail Assignment under Recovery Yifan Xu, Sebastian Wandelt & Xiaoqian Sun
(Beihang University)
12:45 Lunch
SYSTEMS AND TOOLS TO IMPROVE ATM PERFORMANCE - Session Chair: Jacco
Hoekstra (TU Delft)
Time Paper Title Authors (presenter in bold)
14:00 83 Accrued Delay Application in Trajectory-Based Husni Idris (NASA), Christopher Chin (SGT) &
Operations Antony Evans (Crown Consulting)
14:45 93 Alternative Resource Allocation Mechanisms for Alexander Estes (University of Minnesota) &
the Collaborative Trajectory Options Program Michael Ball (University of Maryland)
(CTOP)
15:30 Coffee
COMPLEXITY - Session Chair: Dirk Schaefer (EUROCONTROL)
Time Paper Title Authors (presenter in bold)
16:00 59 Predicting and Analyzing US Air Traffic Delays Philippe Monmousseau, Daniel Delahaye
using Passenger-centric Data-sources (ENAC), Aude Marzuoli & Eric Feron (Georgia
Institute of Technology)
16:45 97 Causal Demand Modelling for Applications in Ivan Tereshchenko & Mark Hansen (UC Berkeley)
En Route Air Traffic Management
19:00 Gala dinner
19Friday 21 June 2019
PLENARY CLOSING SESSION - Grand Waterfront Hall
08:00 Perspectives from US and Europe
The US Perspective - Joseph Post (FAA)
The European Perspective - Paul Bosman (EUROCONTROL)
News from the SESAR Knowledge Transfer Network Engage - Andrew Cook (University of
Westminster)
09:30 Coffee
10:00 Panel: Where will Urban Air Mobility be in 20 years? - Grand Waterfront Hall
Moderators: Sandy Lozito (NASA) and Munish Khurana (EUROCONTROL)
Participants: François Sillion (Uber Elevate), R. John Hansman (MIT), Natesh Manikoth (FAA),
Parimal Kopardekar (NASA), Joerg P. Mueller (Airbus)
11:30 Closing Session
Best Paper Awards including selected Best Paper Teaser Presentations
Announcement ICRAT 2020 and ICRAT Student Grand Challenge
Closing and Announcement ATM Seminar 2021 - Eric Neiderman (FAA) and Dirk Schaefer
(EUROCONTROL)
13:00 Lunch
Committee Meeting (working lunch)
20Abstracts
21Abstracts: Trajectory Management
Integrated Time-Based Management and Performance-Based Navigation Design for Trajectory-Based
Operations – Roland M. Sgorcea, Lesley A. Weitz, Ryan W. Huleatt (MITRE), Ian M. Levitt & Robert E. Mount
(FAA)
The Federal Aviation Administration (FAA) is in the process of developing and deploying a concept called Trajectory-
based Operations (TBO), which, among other goals, aims to provide greater predictability and efficiency to flights
by increasing the use of Performance-based Navigation (PBN) procedures and Time-based Management (TBM). To
fully achieve the benefits from TBO operations, PBN procedure designs and TBM designs must be tightly integrated.
To achieve some of the initial TBO objectives that have been identified (i.e., improvements in throughput, predict-
ability, flight efficiency, and flexibility), the research presented here makes the case that PBN and TBM design must
be considered together. An integrated design philosophy is needed to ensure: PBN procedures support Air Traffic
Control (ATC) in managing trajectories using speed control only; TBM adaptation yields feasible schedules and accu-
rate information for ATC’s management of flights; and predictable paths support pilots’ energy management task
throughout the arrival and approach. This paper will outline the case for creating an integrated PBN and TBM design
process and associated tools to help ensure TBM and PBN goals can be fully realized. The paper also includes three
design examples that demonstrate the need for an integrated design process and supporting design tools.
Enroute Traffic Overflows versus Arrival Management Delays – Raphaël Christien, Eric Hoffman & Karim
Zeghal (EUROCONTROL)
The MITRE Corporation’s Center for Advanced Aviation System Development (MITRE/CAASD) made improvements to
the Risk Analysis Process (RAP) Tool scoring methods used in quantifying the risk level for loss of separation events.
These enhancements are designed to evolve the present scoring methods by using operational data for trend anal-
ysis and promoting increased safety through risk mitigation and management. The new changes aim to simplify
the tool’s use and eliminate any potential biases associated with it. A newly modified RAP Tool has been developed
for future evaluation of events and it closely aligns with the current Federal Aviation Administration (FAA) Safety
Management System (SMS) Risk Matrix. The tool will be used by the FAA to closer examine the risk involved in loss of
separation events in order to better prioritize their mitigations.
Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options –
Antony D. Evans (Crown Consulting) & Paul U. Lee (NASA)
The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight
operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotia-
tion between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and
demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high proba-
bility of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route
candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight
plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability
of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in
flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing
algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an
historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.
22Abstracts: Separation
EnAcT: Generating Aircraft Encounters using a Spherical Earth Model – James A. Ritchie III, Andrew J.
Fabian (FAA), Nidhal C. Bouaynaya (Rowan University) & Mike M. Paglione (FAA)
There is ongoing research at the Federal Aviation Administration (FAA) and other private industries to examine a
concept for delegated separation in multiple classes of airspace to allow unmanned aircraft systems (UAS) to remain
well clear of other aircraft. Detect and Avoid (DAA) capabilities are one potential technology being examined to
maintain separation. To evaluate these DAA capabilities, input traffic scenarios are simulated based on either simple
geometric aircraft trajectories or recorded traffic scenarios and are replayed in a simulator. However, these approaches
are limited by the breadth of the traffic recordings available. This paper derives a new mathematical algorithm that
uses great circle navigation equations in an Earth spherical model and an accurate aircraft performance model to
generate realistic aircraft encounters in any airspace. This algorithm is implemented in a program called Encounters
from Actual Trajectories (EnAcT) and uses a number of user inputs defining the encounter events, called encounter
properties. Given these encounter properties, the program generates two 4dimensional flight trajectories that satisfy
these properties. This encounter generator could be used to evaluate DAA systems as well as initiate research in
automation for conflict detection and resolution.
Learning Real Trajectory Data to Enhance Conflict Detection Accuracy in Closest Point of Approach
Problem – Zhengyi Wang (ENAC), Man Liang (University of South Australia), Daniel Delahaye & Weilu Wu
(ENAC)
Closest Point of Approach (CPA) is one of the main problems in aircraft Conflict Detection (CD). It aims to find out
the minimum distance and the associated time between two aircraft on the same altitude with crossing traffic.
Conventional CPA prediction model generally assumes that the speed and heading of the aircraft are constant. But
the uncertainties in real operations lead to the inaccuracy of CPA prediction. In this paper, we introduce a novel CD
framework with Machine Learning (ML) methods. It aims to improve the CPA prediction accuracy with the help of real
trajectory data. The new model contributes to not only reduce the number of fault Short-mid term conflict alert for
air traffic controllers but also support the implementation of future free flight concept, so as to reduce fuel consump-
tion and emission. In our study, we firstly propose a data processing method to generate a close-to-reality simulation
data from Mode-S observations. Then, feature engineering is used to transform the raw data into suitable features,
which will enable the ML models to make predictions with high-performance. Six prevailing ML methods (MLR, SVM,
FFNNs, KNN, GBM, RF) are used to predict the CPA time and distance. Their prediction results are compared with the
conventional CPA model (baseline). The simulation results demonstrate that the GBM is the best prediction model
both in CPA prediction and conflict detection. However, the results also prove that not all the ML models outperform
the baseline CPA model. Suitable ML methods can greatly enhance the conflict detection accuracy.
A Machine Learning Approach for Conflict Resolution in Dense Traffic Scenarios with Uncertainties –
Duc-Thinh Pham, Ngoc Phu Tran, Sameer Alam, Vu Duong (Nanyang Technological University) & Daniel
Delahaye (ENAC)
With the continuous growth in the air transportation demand, air traffic controllers will have to handle increased
traffic and consequently more potential conflicts, and this gives rise to the need of conflict resolution tools that can
preform well in high density traffic scenario given a noisy environment. Unlike model-based approaches, learning-
based or machine learning approaches can take advantage of historical traffic data and flexibly encapsulate the
environmental uncertainty in performing conflict resolution. In this study, we propose an artificial intelligent agent
that is capable of resolving conflicts, in the presence of traffic and given uncertainties in conflict resolution maneu-
vers, without the need of prior knowledge about a set of rules mapping from conflict scenarios to expected actions.
The conflict resolution task is formulated as a decision-making problem in large and complex action space, which is
applicable for employing reinforcement learning algorithm. Our work includes the development of a learning envi-
ronment, scenario state representation, reward function, and learning algorithm. As the result, the proposed method,
inspired from Deep Q-learning and Deep Deterministic Policy Gradient algorithms, is able to resolve conflicts, with a
success rate of over 81%, in the presence of traffic and varying degrees of uncertainties.
23Guaranteed Conflict: When Speed Advisory doesn’t Work for Time-based Flow Management – Xiyuan Ge
(University of Washington), Minghui Sun & Cody Fleming (University of Virginia)
Time-based Flow Management (TBFM) is one of the core portfolios of the Next Generation Air Transportation System
(NextGen). However, according to multiple reports, there is general confusion about the usage and implementation
of the time-based capabilities. This paper aims at answering questions about the usage of time-based instructions
and speed advisories to maintain safe distances for TBFM. Towards this end, three collectively exclusive types of situ-
ation which are “conflict free”, “potential conflict” and “guaranteed conflict” are developed to classify the condition
of a flow of aircraft. Then, a decision-making process is further proposed using the three classes to increase the use
of time-based instructions and speed adjustment and avoid the costly vectoring and path stretching. Furthermore,
algorithms are developed to assist the process in identifying the “guaranteed conflict” and resolving the conflict by
removing the least number of airplanes from the flow. Lastly, a use case is studied to illustrate the decision-making
process and the effectiveness of the proposed algorithms.
Automation for Separation with CDOs: Dynamic Aircraft Arrival Routes – Raúl Sáez, Xavier Prats (UPC),
Tatiana Polishchuk, Valentin Polishchuk & Christiane Schmidt (Linköping University)
We present a mixed-integer programming (MIP) approach to compute aircraft arrival routes in a terminal maneu-
vering area (TMA) that guarantee temporal separation of all aircraft arriving within a given time period, where the
aircraft are flying according to the optimal continuous descent operation (CDO) speed profile with idle thrust. The
arrival routes form a merge tree that satisfies several operational constraints, e.g., all merge points are spatially
separated. We detail how the CDO speed profiles for different route lengths are computed. Experimental results are
presented for calculation of fully automated CDO-enabled arrival routes during one hour of operation on a busy day
at Stockholm TMA.
24Abstracts: Integrated Airport/
Airside Operations
Field Evaluation of the Baseline Integrated Arrival, Departure, and Surface Capabilities at Charlotte
Douglas International Airport – Yoon C. Jung, William J. Coupe, Al Capps, Shawn Engelland & Shivanjli
Sharma (NASA)
NASA is currently developing a suite of decision support capabilities for integrated arrival, departure, and surface
(IADS) operations in a metroplex environment. The effort is being made in three phases, under NASA’s Airspace
Technology Demonstration 2 (ATD-2) sub-project, through a close partnership with the Federal Aviation Administration
(FAA), air carriers, airport, and general aviation community. The Phase 1 Baseline IADS capabilities provide enhanced
operational efficiency and predictability of flight operations through data exchange and integration, tactical surface
metering, and automated coordination of release time of controlled flights for overhead stream insertion. The users
of the IADS system include the personnel at Charlotte Douglas International Airport (CLT) air traffic control tower,
American Airlines ramp tower, CLT terminal radar approach control (TRACON), and Washington Center. This paper
describes the Phase 1 Baseline IADS capabilities and field evaluation conducted at CLT from September 2017 for a
year. From the analysis of operations data, it is estimated that 538,915 kilograms of fuel savings, and 1,659 metric
tons of CO2 emission reduction were achieved during the period with a total of 944 hours of engine run time reduc-
tion. The amount of CO2 savings is estimated as equivalent to planting 42,560 urban trees. The results have also
shown that the surface metering had no negative impact on on-time arrival performance of both outbound and
inbound flights. The technology transfer of Phase 1 Baseline IADS capabilities has been made to the FAA and aviation
industry, and the development of additional capabilities for the subsequent phases is underway.
Assessment of the Airport Operational Dynamics Using a Multistate System Approach – Álvaro Rodríguez-
Sanz, José Manuel Cordero (CRIDA), Beatriz Rubio Fernández, Fernando Gómez Comendador & Rosa
Arnaldo Valdés (UPM)
The analysis of the airport operational reliability is fundamentally linked to the knowledge of the system’s behavior
and dynamics. This paper proposes a model for assessing airport performance at a tactical level (time scale), focusing
on the airspace-airside turnaround operations (space scale) and considering different areas: delay, capacity, envi-
ronmental impact and operational complexity. Airports are transportation systems that can complete their tasks
with partial performance levels: failures of some system elements may lead to partial degradation of the system
behavior, which cannot be assessed with the traditional binary reliability view (working – not working). To consider
this performance granularity, our model uses a multistate approach. A Markov-chain based methodology allows us
to predict the system’s reliability evolution and move from reactionary measures to predictive interventions. It also
considers the impact of stochasticity on performance prediction by assessing the system operational dynamics. The
methodology is developed through a case study at a major European hub airport: a collection of 160,460 turnaround
operations (registered at 2016) is used to statistically determine the system characteristics. Results for the appraised
case study show that the airport tends to evolve towards repaired states, and that delays are major drivers for airport
performance dynamics. The contribution of the paper is twofold: it presents a new methodological approach to
evaluate airport operational dynamics and it also provides insights on how different factors influence performance.
A Comparative Analysis of Departure Metering at Paris (CDG) and Charlotte (CLT) Airports – Sandeep
Badrinath, Hamsa Balakrishnan (MIT), Ji Ma & Daniel Delahaye (ENAC)
Departure metering has the potential to mitigate airport surface congestion and decrease flight delays. This paper
considers several candidate departure metering techniques, including a trajectory-based optimization approach
using a node-link model and three aggregate queue-based approaches (a scheduler based on NASA’s ATD-2 logic, an
optimal control approach, and a robust control approach). The outcomes of these different approaches are compared
for two major airports: Paris Charles De Gaulle airport (CDG) in Europe and Charlotte Douglas International airport
(CLT) in the United States. Stochastic simulations are used to show that the robust control approach best accom-
modates operational uncertainties, while all the approaches considered yield higher taxi-out time savings at CLT
compared to CDG.
25Departure Management with Robust Gate Allocation – Ruixin Wang, Cyril Allignol, Nicolas Barnier & Jean-
Baptiste Gotteland (ENAC)
The Airport Collaborative Decision Making (A-CDM) concept yields concrete and promising solutions for airports,
in terms of traffic punctuality and predictability, with possible delay, noise and pollution reduction. A key feature
of A-CDM is the Departure Management (DMAN): runway take-off sequences can be anticipated such that a signifi-
cant part of the delay can be shifted at the gate, engines off, without penalizing the remaining traffic. During this
process, an increase in the gate occupancy for delayed departures is unavoidable, therefore the airport layout must
provide enough gates and their allocation must be robust enough w.r.t. departures delay. In this paper, we introduce
a method to estimate the gate delays due to the DMAN pre-departure scheduling, then we propose a robust gate
allocation algorithm and assess its performance with current and increased traffic at Paris-Charles-de-Gaulle interna-
tional airport. Results show a significant reduction in the number of gate conflicts, when comparing such a robust
gate allocation to current practice.
Impact of Stochastic Delays, Turnaround Time and Connection Time on Missed Connections at Low Cost
Airports – Hasnain Ali, Yash Guleria, Sameer Alam, Vu N. Duong (Nanyang Technological University) &
Michael Schultz (DLR)
Low cost carriers usually operate from budget terminals which are designed for quick turn around, faster passenger
connections with minimal inter-gate passenger walking distance. Such operations are highly sensitive to factors
such as delays, turnaround-time and flight connection time and may lead to missed connections for transfer passen-
gers. In this paper, we propose a framework to analyze the effect of turnaround times, minimum connection times
and stochastic delays on missed connections. We use Singapore Changi Airport budget terminal as a case study to
demonstrate the impact of operational uncertainties on the passenger connections, considering an optimal gate
assignment, using heuristic search, for scheduled arrivals and departures. Results show that by increasing turnaround
time and minimum connection time and by reducing delays, the chances of missed connections can be significantly
reduced. Specifically by maintaining the flight turnaround time at 50 min, minimum connection time at 60 min and
by containing arrival delays within 70% of the current delay spread, transfer passenger missed connections can be
prevented for almost all the flights. The proposed method also helps identify the gates which are more prone to
missed connections given operational uncertainties and flight scenarios.
26Abstracts: Safety
Analysis of Safety Performances for Parallel Approach Operations with Performance-based Navigation
– Stanley Förster, Hartmut Fricke (TU Dresden), Bruno Rabiller, Brian Hickling, Bruno Favennec & Karim
Zeghal (EUROCONTROL)
This paper presents a sensitivity analysis of safety performances for independent parallel approach operations, using
performance based navigation (PBN) transitions connecting to final approaches still relying on ground based landing
system (ILS, MLS or GLS). The analysis relies on a stochastic modelling (Monte Carlo simulations), addressing both
normal and non-normal (blunder) operations, with a total of 1.700.000 runs for normal operations and 180.000.000
runs for non-normal. The focus is on the intercept phase with two parameters considered: runway spacing and
location of the intermediate fix. The results indicate that, assuming a lower blunder rate, performance based naviga-
tion transitions to final provides a better safety performance in terms of loss of separation and risk of collision than
vectoring to final. They also reveal that the risk of collision with performance based navigation to final is more sensi-
tive to the location of the intermediate fix, thus requiring a careful design.
Modelling Go-Around Occurrence – Lu Dai, Yulin Liu & Mark Hansen (UC Berkeley)
Go-around is an aborted landing of an aircraft that is on final approach. In this work, we model the impact of separa-
tion, airport condition, weather condition, and trajectory performance on go-around occurrence. A trajectory-based
go-around detection algorithm has been developed and applied to the last three-quarter of JFK arrival flights in
2018. Principal component regression (PCR) model, with a retrospective causal inference design, has been estimated
and further been used in counterfactual scenarios to reveal the causal correlations between factors of interest and
go-around occurrence. Our results suggest that airport visibility and ceiling, flight perpendicular distance to the
Extended Runway Centerline (ERC) are the two most salient factors in causing go-arounds.
What is the Potential of a Bird Strike Advisory System? – Isabel C. Metz (DLR & TU Delft), Thorsten
Mühlhausen (DLR), Joost Ellerbroek (TU Delft), Dirk Kügler (DLR) & Jacco M. Hoekstra (TU Delft)
This paper presents a collision avoidance algorithm to prevent bird strikes for aircraft departing from an airport. By
using trajectory-information of aircraft and birds, the algorithm predicts potential collisions. Collision avoidance is
performed by delaying departing aircraft until they can follow a collision-free trajectory. An implementation of this
concept has the potential to increase aviation safety by preventing bird strikes but might reduce runway capacity
due to delaying aircraft. As a precursor to the feasibility, this study investigates the maximum achievable safety effect
at minimum delay costs of such a system by assuming a deterministic world. Therefore, no uncertainties regarding
bird and aircraft positions were considered to enable the system to prevent all bird strikes for departing traffic while
causing the smallest possible delay. The anticipated effects were studied by running fast-time simulations including
three air traffic intensities at a single-runway airport and bird movements from all seasons. The results imply a high
potential for the increase in safety at a reasonable reduction in runway capacity. An initial cost-estimate even revealed
a strong saving potential for the airlines. Based on these results, a feasibility study of implementing a bird strike advi-
sory system including uncertainties in bird movements as well as probabilistic effects will be performed.
Development of a Collision Avoidance Validation and Evaluation Tool (CAVEAT): Addressing the Intrinsic
Uncertainty in TCAS II and ACAS X – Sybert Stroeve, Henk Blom (NLR), Carlos Hernandez Medel, Carlos
García Daroca, Alvaro Arroyo Cebeira (everis) & Stanislaw Drozdowski (EUROCONTROL)
Airborne Collision Avoidance Systems (ACAS) form a key safety barrier by providing last-moment resolution adviso-
ries (RAs) to pilots for avoiding mid-air collisions. For the generation of advisories ACAS uses various ownship state
estimates (e.g. pressure altitude) and othership measurements (e.g. range, bearing). Uncertainties, such as noise in
ACAS input signals and variability in pilot performance imply that the generation of RAs and the effectuated aircraft
trajectories are non-deterministic processes. These can be analysed effectively by Monte Carlo (MC) simulation of
the various uncertainties in encounter scenarios. Existing ACAS simulation tools reflect the intrinsic uncertainties to
a limited extent only. In recognition of the need of an ACAS evaluation tool that supports MC simulation of these
uncertainties, this paper develops an agent-based model, which captures uncertainties in ACAS input and pilot
performance for the simulation of encounter scenarios, while using ACAS algorithms (TCAS II, ACAS Xa). The novel
ACAS evaluation tool is named CAVEAT (Collision Avoidance Validation and Evaluation Tool). Through illustrative MC
simulation results it is demonstrated that the uncertainties can have significant effect on the variability in timing and
types of RAs, and subsequently on the variability in the closest point of approach (CPA). It is shown that even mean
results of MC simulation can differ significantly from results of a deterministic simulation. Most importantly, the tails
of CPA probability distributions are affected. This stipulates that addressing all intrinsic uncertainties through MC
simulation is essential for proper evaluation of ACAS.
27Abstracts: Complexity
Cost Reductions enabled by Machine Learning in ATM – Hartmut Helmke, Matthias Kleinert, Jürgen Rataj
(DLR), Petr Motlicek (Idiap), Christian Kern (Austro Control), Dietrich Klakow (Saarland University) & Petr
Hlousek (Air Navigation Services of the Czech Republic)
Various new solutions were recently implemented to replace paper flight strips through different means. Therefore,
digital data comprising instructed air traffic controller (ATCO) commands can be used for various purposes. This
paper summarizes recent works on developing speech recognition systems to automatically transcribe commands
issued by air-traffic controllers to pilots allowing decrease of ATCOs’ workload, which leads to significant increase of
ATM efficiency and cost savings. First experiments in AcListant® project have validated that Assistant Based Speech
Recognition (ABSR) integrating a conventional speech recognizer with an assistant system can provide an adequate
solution. The following EC H2020 funded MALORCA project has proposed new Machine Learning algorithms
significantly reducing development and maintenance costs while exploiting new automatically transcribed speech
corpora. In this paper, besides recapitulating achieved recognition performance for Prague and Vienna approach,
new statistics obtained from various error analysis processes are presented. Results are detailed for different types of
ATC commands followed by rationales causing the performance drops.
Characterizing National Airspace System Operations Using Automated Voice Data Processing – Shuo
Chen, Hunter Kopald, Rob Tarakan, Gaurish Anand & Karl Meyer (MITRE)
Air Traffic Control (ATC) radio communications contain a wealth of situational context information. While valu-
able, this information resource has been difficult and expensive to use for large scale analyses because raw speech
audio cannot be directly used in analyses without human or computer interpretation. To help the Federal Aviation
Administration (FAA) better understand National Airspace System (NAS) dynamics, The MITRE Corporation (MITRE)
has been developing voice data analysis capabilities that can enable information from ATC voice communications to
be automatically processed and used in post-operational analyses. These capabilities use an array of technologies to
segment audio data by speaker role, transcribe the audio to text, and extract semantic entities such as aircraft identi-
fiers and clearances. The data derived by these capabilities can inform large-scale analyses, augmenting existing data
sources such as radar tracks and flight plans, and enable studies and the generation of metrics that were previously
impractical. This paper describes these voice data processing capabilities and presents one example of the use of
voice data: to enable better understanding of Performance-Based Navigation (PBN) procedure utilization in the NAS.
This paper describes an initial use of voice data analysis to better understand approach procedure utilization, which
opens the door for many new analyses.
Clustering Aircraft Trajectories on the Airport Surface – Andrew Churchill & Michael Bloem (Mosaic)
In this paper, we describe an approach for clustering aircraft taxi trajectories on the airport surface. The resulting
clusters can enable improved or novel analyses and optimization of airport surface traffic. In particular, we seek
to identify anomalous taxi trajectories. While statistically anomalous trajectories may be planned or expected by a
human controller, they may also be unplanned, and thus may represent flights that could pose safety risks. We devel-
oped a novel hierarchical clustering algorithm that groups taxi paths in space and then in time. We present results
for Charlotte Douglas International Airport (KCLT), showing the common taxi trajectories represented by the clus-
ters, and then discuss leveraging those clusters to identify anomalous trajectories in this dataset. This unsupervised
machine learning approach is able to successfully differentiate between typical and anomalous trajectories in a post
hoc setting. We have begun to validate the anomalies with subject matter experts as being a combination of infre-
quently-used paths and true anomalies. In addition, by clustering in time the trajectories in a shape-based cluster, we
can separate free-flowing trajectories from those with stops and identify some common stopping points. Finally, we
identify numerous extensions of this approach, and other applications for the underlying clustering methodology.
Identifying Anomalies in past en-route Trajectories with Clustering and Anomaly Detection Methods –
Xavier Olive & Luis Basora (ONERA)
This paper presents a framework to identify and characterise anomalies in past en-route Mode~S trajectories. The
technique builds upon two previous contributions introduced in 2018: it combines a trajectory-clustering method
to obtain the main flows in an airspace with autoencoding artificial neural networks to perform anomaly detec-
tion in flown trajectories. The combination of these two well-known Machine Learning techniques (ML) provides a
useful reading grid associating cluster analysis with quantified level of abnormality. The methodology is applied to
a sector of the French Bordeaux Area Control Center (ACC) during its 385 hours of operation over seven months of
ADS-B traffic. The results provide a good taxonomy of deconfliction measures and weather-related ATC actions. The
application of this work is manyfold, ranging from safety studies estimating risks of midair collision, to complexity
28and workload assessments of traffic when a sector is operated, or to the constitution of a database of ATC actions
ensuring aircraft separation. This database could be used to train further ML techniques aimed at improving the state
of the art of deconfliction algorithms.
Data-Driven Precursor Detection Algorithm for Terminal Airspace Operations – Raj Deshmukh, Dawei Sun
& Inseok Hwang (Purdue University)
The air traffic management system is one of the most complex man-made systems, with stringent standards for
safety and operational performance. Modern surveillance systems make available detailed flight and airport infor-
mation, through on-board and ground recording systems. These recorded datasets can be used for detecting and/
or predicting anomalies which hinder safe and efficient operations. The prediction of an anomaly is performed by
identifying events that precede the occurrence of an anomaly, which are called precursors. In this paper, we propose
a detection algorithm that can identify precursors for flight anomalies through data-driven models designed with
surveillance data recorded in the terminal airspace. The proposed algorithm is demonstrated to detect precursors
of flight anomalies in the terminal airspace around LaGuardia (LGA) airport in New York City using real traffic data
obtained from the Airport Surface Detection Equipment - Model X (ASDE-X) and the Terminal Automation Information
Service (TAIS) surveillance datasets.
Predicting and Analyzing US Air Traffic Delays using Passenger-centric Data-sources – Philippe
Monmousseau, Daniel Delahaye (ENAC), Aude Marzuoli & Eric Feron (Georgia Institute of Technology)
This paper aims at presenting a novel way of predicting and analyzing air traffic delays using publicly available data
from social media with a focus on Twitter data. Three different machine learning regressors have been trained on this
2017 passenger-centric dataset and tested for the prediction up to five hours ahead of air traffic delays and cancel-
lations for the first two months of 2018. Comparing and analyzing different accuracy measures of their prediction
performances show that this dataset contains useful information about the current state and short-term future state
of the air traffic system. The resulting methods yield higher prediction accuracy than traditional state-of-the-art and
off-the-shelf time-series forecasting techniques performed on flight-centric data. Moreover a post-training feature
importance analysis conducted on the Random Forest regressor allowed a simplification and a refining of the model,
leading to a faster training time and more accurate predictions. This paper is a first step in predicting and analyzing
air traffic delays leveraging a real-time publicly available passenger-centered data source. The results of this study
suggest a method to use passenger-centric data-sources both as an estimator of the current state of air traffic delays
as well as an estimator of the short-term state of air traffic delays in the United States in real-time.
Causal Demand Modelling for Applications in En Route Air Traffic Management – Ivan Tereshchenko &
Mark Hansen (UC Berkeley)
Increasing air traffic volume makes en route Traffic Management Initiatives (TMIs) more important than ever before.
The effective execution of en route TMIs depends on accurate predictions of airspace demand. Precise forecasts
of airspace demand require causal models of route choice. Previous research shows that obtaining such models
is extremely difficult, due to the complex nature of the airspace system. In this paper, we test three methods for
making causal estimates of route utility in the context of two en route TMIs – the Airspace Flow Program (AFP)
and Collaborative Trajectory Options Program (CTOP). The testing was done using simulated TMI data. We show that
statistical models of the behavior of individual flights produce biased estimates of route utility. Models based on
changes in aggregate delay produce better estimates; however, such models are harder to implement in practice.
Finally, CTOP offers data structures that allow us to achieve higher quality airspace demand predictions.
29Abstracts: Network and Strategic
Flow Management
A Novel Air Traffic Flow Management Model to Optimise the Network Delay – Sergio Ruiz, Hamid Kadour
& Peter Choroba (EUROCONTROL)
This paper describes the Interacting Regulations problem and a new method is presented to analyse and opti-
mise the network delay. The aim of this research is to contribute to enhance the Computer Assisted Slot Allocation
(CASA) mechanism used today in Europe for assigning Air Traffic Flow and Capacity Management (ATFCM) slots. The
Interacting Regulations problem appears during congestion periods due the non-smoothed coordination of multiple
ATFCM constraints applied locally at different sectors. Flights affected by multiple regulated sectors may change
their default first-plan-first-served (FPFS) sequence position in some regulated sectors, which may generate complex
‘interactions’ –positive or negative– between those regulations that can typically increase the total delay in the
network. An enhanced slot allocation method referred as Enhanced CASA (ECASA) is proposed in this paper, which
consists in optimising the default CASA sequences by applying small slot amendments to some selected flights. Early
benchmarking of the ECASA performance show that the optimisation strategies introduced could notably reduce the
delay to AUs (27% in average in the simulated period of summer 2018); the proportion of flights delayed more than
10 minutes could also be notably reduced (38%), thus reducing the cost of operations.
Operational Concept of Traffic Pattern Classifier for Optimal Ground Holding – Adriana Andreeva-Mori &
Naoki Matayoshi (JAXA)
A dual-component ground holding (GH) algorithm based on real-time air traffic classification and offline ground
holding program parameter optimization is proposed. Numerical simulations are developed to quantitatively
evaluate this new concept. GH program performance is evaluated based on airborne delay, ground delay, and lost
throughput costs. Preliminary results show that the developed machine-learning-based traffic pattern classifier can
propose ground holding control parameters which would result in savings within mean absolute percentage error of
17.96% of the potential optimal ones.
Airway Network Flow Management using Braess’s Paradox – Qing Cai, Chunyao Ma, Sameer Alam, Vu N.
Duong (Nanyang Technological University) & Banavar Sridhar (NASA)
The ever increasing demand for air travel is likely to induce air traffic congestion which will elicit great economic
losses. In the presence of limited airspace capacity as well as the saturated airway network, it is no longer feasible to
mitigate air traffic congestion by adding new airways/links. In this paper, we provide a ``counter-intuitive’’ perspective
towards air traffic congestion mitigation by removing airways/links from a given airway network. We draw inspira-
tion from Braess’s Paradox which suggests that adding extra links to a congested traffic network could make the
traffic more congested. The paper explores whether Braess’s Paradox occurs in airway networks, or more specifically,
whether it is possible to better distribute the flow in an airway network by merely removing some of its airways/
links. In this paper, We develop a generic method for Braess’s Paradox detection for a given airway network. To vali-
date the efficacy of the method, a case study is conducted, for South-East Asian airspace covering Singapore airway
network, by using 6 months ADS-B data. The results shows that Braess’s Paradox does occur in airway networks and
the proposed method can successfully identify the airway network links that may cause it. The results also demon-
strates that, upon removing such links, the total travel time for a given day traffic at a given flight level, was reduced
from 8661.15 minutes to 8328.64 minutes, a reduction of 332.5 minutes. This amounts to a saving of 3.8% in travel
time.
Strategic Flight Cancellation under Ground Delay program Uncertainty – Christine Taylor, Shin-Lai Tien,
Erik Vargo & Craig Wanke (MITRE)
Under certain capacity constraints, flight operators will strategically cancel flights to improve their overall operating
schedule. However, the benefits of such cancellations are best realized if made early, often before any traffic flow rate
limitation is imposed. With improved weather forecasts, the need for early action is more apparent; however, deter-
mining the correct actions – in this case, flight cancellations – is still challenging. This paper proposes a framework
for optimizing an adaptive decision strategy based on the evolution of the forecast uncertainty. Using an ensemble
forecast, a scenario tree is generated to highlight both key planning scenarios and the likelihood of these scenarios
developing over the forecast horizon. By aligning decision points at the initial and intermediary nodes in the tree,
strategies are optimized to capture the timing of relevant decisions with respect to the forecast uncertainty. Using
flight cancellation under Ground Delay Program uncertainty as an example, the paper will analyze the recommended
cancellations over the forecast horizon, against different predicted scenarios as well as how these recommendations
adapt as new forecast information is made available. The results will show that by directly planning for adaptation,
improved outcomes can be obtained.
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