=Paper=
{{Paper
|id=Vol-2536/om2019_Tpaper1
|storemode=property
|title=Matching Ontologies for Air Traffic Management: a Comparison and Reference Alignment of the AIRM and NASA ATM Ontologies
|pdfUrl=https://ceur-ws.org/Vol-2536/om2019_LTpaper1.pdf
|volume=Vol-2536
|authors=Audun Vennesland,Richard M. Keller,Christoph G. Schuetz,Eduard Gringinger,Bernd Neumayr
|dblpUrl=https://dblp.org/rec/conf/semweb/VenneslandKSGN19
}}
==Matching Ontologies for Air Traffic Management: a Comparison and Reference Alignment of the AIRM and NASA ATM Ontologies==
https://ceur-ws.org/Vol-2536/om2019_LTpaper1.pdf
Matching Ontologies for Air Traffic Management:
A Comparison and Reference Alignment of the
AIRM and NASA ATM Ontologies
Audun Vennesland1,2 , Richard M. Keller3 , Christoph G. Schuetz4(B) ,
Eduard Gringinger5 , and Bernd Neumayr4
1
Norwegian University of Science and Technology
audun.vennesland@ntnu.no
2
SINTEF, Trondheim, Norway
3
Intelligent Systems Division, NASA Ames Research Center, Moffett Field, CA, USA
rich.keller@nasa.gov
4
Johannes Kepler University Linz, Linz, Austria
{christoph.schuetz,bernd.neumayr}@jku.at
5
Frequentis AG, Vienna, Austria
eduard.gringinger@frequentis.com
Abstract. Air traffic management (ATM) relies on the timely exchange
of information between stakeholders to ensure safety and efficiency of
air traffic operations. In an effort to achieve semantic interoperability
within ATM, the Single European Sky ATM Research (SESAR) program
has developed the ATM Information Reference Model (AIRM), which
individual information exchange models should comply with. An OWL
representation of the AIRM – the AIRM Ontology (AIRM-O) – facili-
tates applications. Independently from the European efforts, the NASA
Air Traffic Management Ontology (ATMONTO) has been developed as
an RDF/OWL ontology representing ATM concepts to facilitate data
integration and analysis in support of NASA aeronautics research. Concep-
tualization mismatches between the AIRM-O and ATMONTO ontologies
– mostly due to different design decisions, but also as a consequence of the
different regulatory systems and philosophies underlying ATM in Europe
and the United States – pose a challenge to automatic ontology matching
algorithms. In this paper, we describe mismatches between AIRM-O and
ATMONTO, evaluate performance of automatic matching systems over
these ontologies, and provide a manual reference alignment.
1 Introduction
Modern air traffic management (ATM) employs standardized models for the
exchange of information required for seamless air traffic operations. Each ex-
change model has a different focus. The Aeronautical Information Exchange
Model (AIXM) [1], for example, facilitates the representation of messages for
pilots and air traffic controllers notifying of important events such as temporary
runway closures and malfunctions of navigation aids. The exchange models are
Copyright c 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 A. Vennesland et al.
subject to constant evolution in various standards working groups. In this regard,
maintaining consistent co-evolution of the different exchange models is a necessity
not only to guarantee efficiency of operations – by ensuring interoperability of
systems – but also for safety reasons.
Recognizing the necessity of a common reference for the constantly evolving
exchange models, the Single European Sky ATM Research (SESAR) program
established the ATM Information Reference Model (AIRM) [25], developed under
supervision of EUROCONTROL in an effort with industry and academia but
meanwhile also adopted by the International Civil Aviation Organization (ICAO).
The individual exchange models must ensure compliance with AIRM.
The AIRM Ontology (AIRM-O) [21] is an OWL ontology derived from the
UML representation of AIRM in an effort to facilitate operationalization of AIRM.
In this regard, previous work has investigated automatic compliance validation
between exchange models and AIRM [22] as well as the annotation of ATM data
sources with a semantic description of the contents [15].
The NASA Air Traffic Management Ontology (ATMONTO) [12, 13] supports
NASA’s aeronautics research activities by facilitating integration of data from
various sources for analysis purposes. Developed independently from AIRM with
a different purpose and under a different regulatory system – the United States
instead of Europe – the question arises to what extent ATMONTO is actually
compatible with AIRM-O.
In order to link AIRM-O and ATMONTO, we manually produced a reference
alignment between these ontologies. In the course of the alignment process,
we identified different types of mismatches between AIRM-O and ATMONTO,
which we relate to existing mismatch classifications from literature. During the
manual mapping process, we also experimented with state-of-the-art ontology
matching systems. Some of the encountered mismatches pose a serious challenge
for automatic ontology matching systems. According to the results from some
of the benchmarks organised by the Ontology Alignment Evaluation Initiative
(OAEI), the performance of ontology matching systems has improved significantly
over recent years [7]. In some tracks, several of the competing systems achieve
close to perfect F-measure [5], i.e., they are able to identify almost all relations in
the track’s ground truth alignment without producing false positives. Matching
the two ATM ontologies, however, proved somewhat difficult for these systems.
Some of the tested systems identified very few but correct relations whereas
others identified a couple of more correct relations, but included too many
incorrect relations. The reference alignment between ATMONTO and AIRM-O
may serve the ontology matching community as a gold standard for improving
and evaluating matching algorithms.
The remainder of this paper is organized as follows. In Sect. 2 we present
relevant background information about the investigated ATM ontologies. In
Sect. 3 we introduce a reference alignment between ATMONTO and AIRM-O.
In Sect. 4 we identify mismatches between the ontologies. In Sect. 5 we evaluate
performance of automatic matching systems. In Sect. 6 we review related work.
We conclude with a summary and an outlook on future work.
Matching Ontologies for Air Traffic Management 3
2 Ontologies for Air Traffic Management
The AIRM addresses the issue of semantic interoperability between ATM sys-
tems through harmonized and agreed upon definitions of the information being
exchanged in ATM [25]. The exchanged ATM information must comply with the
AIRM definitions, the individual exchange models are aligned with the AIRM.
AIRM is defined in UML, the various diagrams falling into the following subject
fields: AirTrafficOperations, Aircraft, AirspaceInfrastructure, BaseInfrastructure,
Common, Environment, Flight, Meteorology, Stakeholders, and Surveillance. The
subject fields represent specific concerns of ATM.
In order to facilitate application of AIRM in practice, the SESAR exploratory
research project BEST6 developed the AIRM Ontology (AIRM-O) [21]. AIRM-O
has been semi-automatically derived from the XML Metadata Interchange (XMI)
representation of the AIRM UML diagrams using manual preprocessing and XSL
Transformation (XSLT) scripts to obtain an OWL ontology. The transformation of
the AIRM UML diagrams into an OWL ontology follows the Object Management
Group’s guidelines from the Ontology Definition Metamodel [17].
Independently from AIRM, ATMONTO was developed in the context of
NASA’s aeronautics research activities as a facilitator for data integration and
analysis. ATMONTO supports semantic integration of ATM data being collected
and analyzed at NASA for research and development purposes. The ontology
functions as an integrative superstructure upon which to overlay data from
multiple stove-piped aviation data sources, thus enabling cross-source queries
that would be otherwise time-consuming and costly. ATMONTO includes a
wide range of classes, properties, and relationships covering aspects of flight and
navigation, aircraft equipment and systems, airspace infrastructure, meteorology,
air traffic management initiatives, and other areas.
Development of ATMONTO followed a classic knowledge modeling approach.
First, domain experts identified a core set of aviation data sources to be integrated.
After an analysis of these sources, a proposed set of ATM concepts, properties,
and relations was developed and presented to the experts for critique. The
corresponding revisions led to an initial version of ATMONTO. Since this version
was built in a bottom-up fashion driven by a need to accommodate the core data
sources, the initial ontology did not represent the full complexity of the ATM
domain. Gradually, additional data sources were incorporated, thereby revising
and extending ATMONTO’s set of concepts, properties, and relations. By the end
of the development process, more than ten different data sources were covered
by the ontology, and ATMONTO’s structure had been generalized well beyond
those sources. Although a general model of the ATM domain, ATMONTO’s
development was heavily driven by application requirements. In turn, AIRM-
O’s scope is overall broader than ATMONTO’s since AIRM has been subject
to a more coordinated standardization and governance process inside SESAR,
harmonizing the various ATM information exchange models.
6
Achieving the Benefits of SWIM by Making Smart Use of Semantic Technologies,
https://project-best.eu/
4 A. Vennesland et al.
3 Reference Alignment
In order to develop a reference alignment between AIRM-O and ATMONTO,
a panel of six experts, each having experience within the ATM domain and
knowledge of semantic technologies, collaboratively produced a mapping between
concepts of the two ontologies. All the experts were asked to match each of the
157 classes in ATMONTO to corresponding classes in the larger AIRM-O – see
Table 1 for statistics about the size of the ontologies – by making use of the
experts’ own domain knowledge as well as all available input, including descriptive
class and property annotations in the ontologies and informative web resources
such as Skybrary7 .
Table 1. Ontology Statistics
Classes Object Properties Data Properties
ATMONTO 157 126 189
AIRM-O 915 1761 494
In addition to identifying equivalence classes, each expert also indicated
subsumption relationships between concepts as well as potential mismatches of
varying degree (see Sect. 4). After the initial matches were compiled, two of the
five experts in the panel reviewed the matches for each ATMONTO class and
produced a consensus mapping holding equivalence relations between classes from
the ontologies. With the consensus mapping as a starting point, the reference
alignment was developed using the following approach:
1. Develop equivalence reference alignment. The consensus mapping described
above is formatted in RDF/XML according to the Alignment Format [3].
2. Develop subsumption reference alignment. Here, the same procedure as in
the OAEI 2011 edition [4] was followed: The two source ontologies were
merged into one single ontology in Protégé. Then OWL equivalentClass
axioms consistent with the mapping described above were manually added
between the corresponding classes in the merged ontology. An automated
reasoner (HermiT) performed subsumption reasoning over the classes in
the merged ontology in order to infer subsumption relations. In addition,
subsumption mappings that were discovered in the manual mapping process
but not identified by the reasoner were included in the reference alignment.
3. Evaluate reference alignments. Once both reference alignments were complete
they were manually inspected for errors and inconsistencies.
The reference alignment between ATMONTO and AIRM-O [20] comes as
two separate alignment files, one holding only equivalence relations and the
other holding only subsumption relations. The equivalence reference alignment
7
https://www.skybrary.aero/
Matching Ontologies for Air Traffic Management 5
contains 32 relations in total and the subsumption reference alignment contains
83 subsumption relations. Only direct subsumption relationships were considered
in the subsumption reference alignment, following the convention used during the
development of the reference alignment for the Oriented Matching track arranged
in OAEI 2011 [4].
4 Mismatches between AIRM-O and ATMONTO
In the course of conducting the manual alignment of ATMONTO and AIRM-O
(see Sect. 3), several of the identified candidate equivalence relations were con-
sidered “light matches” at first. In these cases, an equivalence relation between
the classes was often deemed too strong – despite lexically similar class names
hinting at a relation – given that the experts performed poorly on the alignment
task – as judged by the two reviewing experts. Extensive discussions among
the experts involved in the matching exercise revealed that similar class names
were no guarantee of a correct match. In fact, in approximately 25% of the
identified exact-match pairs in the final reference alignment, the class names did
not have any words in common whereas in approximately 40% of the identified
“light-match” candidate equivalence relations the class names did have words in
common. This may explain partly why automated alignment techniques focusing
on class name similarity did not perform particularly well (see Sect. 5).
The initially identified “light matches” between ATMONTO and AIRM-O
actually represent ontology mismatches. Multiple classification systems for mis-
matchs with varying degrees of detail and often considerable overlap exist in
literature. Figure 1 shows a classification of mismatch types synthesized from
Klein [14] and Visser et al. [23, 24] along with mismatch types encountered during
the manual matching between ATMONTO and AIRM-O. Notwithstanding the
differences between classification systems, there seems to be consensus that the
development of an ontology involves two separate processes and, correspondingly,
two broad categories of mismatches can be distinguished [23, 24]. First, conceptu-
alization mismatches are the result of different interpretations of the represented
domain, leading to different classes, individuals, and relations being modeled in
different ontologies for the same domain. Explication mismatches, on the other
hand, are the result of different specifications of domain interpretations in form
of different terms, modeling styles, and encodings being employed.
One category of conceptualization mismatches concerns differences in model
coverage and scope between ontologies from the same domain, which occur when
two ontologies cover different parts of that domain or the same part at dif-
ferent levels of detail. In this regard, a structure mismatch occurs when two
ontologies distinguishing the same set of classes differ in how they are struc-
tured through relations; we could not find a clear case of structure mismatch
between ATMONTO and AIRM-O. A mismatch concerning differing levels of
detail occurs when one class is modeled in more depth and with greater fidelity
than the other. The ASP M eteorologicalCondition class from ATMONTO and
AerodromeCondition from AIRM-O, for example, both represent meteorological
6 A. Vennesland et al.
Ontology Mismatches
Conceptualisation Explication
Modeling Style Encoding
Model Coverage Terminological
and Granularity
Attribute-type Mismatch
Structure Mismatch Synonyms Concept Description
Differing Level of Detail Terms Mismatch Attribute Assignment Mismatch
Differing Intended Use Terms & Definiens Mismatch Differing Standards
Differing Scope
Paradigm
Homonyms
Concept Scope
Definiens Mismatch
Categorization Mismatch Concept & Definiens Mismatch
Differing Representation
Aggregation-level Mismatch Concept Mismatch
Differing Level of Abstraction Differing Word Senses
Fig. 1. Classification of ontology mismatches, synthesized from Klein [14] (white) and
Visser et al. [23, 24] (light grey), extended with mismatch types encountered when
mapping ATMONTO to AIRM-O (dark grey).
information. ASP M eteorologicalCondition, however, is more detailed, compris-
ing all aspects of sky, wind, visibility, and weather whereas AerodromeConditon
is limited to sky conditions. Different properties and relations of similar classes
may also reflect differences in how the classes are to be used in the context of a
domain application (differing intended uses). For example, ReRouteSegment in
ATMONTO describes an alternative air route option for contingency planning
purposes, whereas RouteSegment describes an actual portion of a route being
flown. Eventually, the differing scope of ontologies may result in a class from the
source ontology lacking a matching class in the target ontology because the class
from the source ontology lies outside the defined scope of the target ontology. An
example of a differing scope is the missing equivalent in AIRM-O for the class
DelayM odel in ATMONTO, which specifies a numerical model of airspace delay
under specific traffic conditions. There is no matching class in AIRM-O because
modeling concerns fall outside the scope of this ontology.
A concept scope conceptualization mismatch occurs when two classes seem
to represent the same concept, yet do not cover exactly the same instances,
although the classes intersect. Categorization mismatches and aggregation-level
mismatches fall into the concept scope mismatch category. A categorization
mismatch occurs when two ontologies include the same class, but each ontology
decomposes the class into different subclasses. ATMONTO’s Airport is equivalent
to AIRM-O’s Aerodrome, however due to different geographical and application-
wise scope Airport includes the subclasses U Sairport and InternationalAirport
whereas Aerodrome has no such subclasses. An aggregation-level mismatch
occurs when two ontologies define the same underlying concept using classes at
different levels of abstraction. A differing level of abstraction is encountered when
the matched classes intersect but some instances are outside the intersection.
Consider, for example, AviationIndustryM anuf acturer in ATMONTO and
Matching Ontologies for Air Traffic Management 7
AerospaceM anuf acturer in AIRM-O. In this case, the term “Aerospace” has a
broader meaning than “Aviation”, hinting at a subsumption relation.
The class of explication mismatches encompasses terminological, modeling
style, and encoding mismatches. In this regard, an encoding mismatch relates to
how the ontologies employ different formatting when describing instances, e.g.,
describing an instance either in miles or kilometres [14]; we omit this mismatch
type in the remainder of this analysis. More relevant for our analysis are the
terminological and modeling-style mismatches identified by Visser et al. [23, 24],
which occur due to different knowledge definitions used in the ontologies and
their associated concepts.
The category of terminological mismatches comprises mismatches related to
synonyms and homonyms. The synonym mismatch as explained by Klein [14]
refers to two lexically different terms in fact meaning the same thing (e.g. ‘Air-
port/Heliport’ versus ‘Aerodrome’), so we do not consider this a real mismatch in
our analysis. Term mismatches as well as terms-and-definiens mismatches defined
by Visser et al. [23, 24] belong to the synonym mismatches. A term mismatch oc-
curs when the definitions share the same concept and the same definiens, but the
terms are different. Correspondingly, a term-and-definiens mismatch occurs when
the definitions refer to the same underlying concept, but the terms and definiens
are different. The relation between Airport in ATMONTO and Aerodrome in
AIRM-O could also be considered a terms-and-definiens mismatch.
Mismatches related to homonyms occur when the meaning of two identical
terms is different (e.g. the term ‘Conductor’ has a different meaning in music than
in electrical engineering). We refer to homonym mismatches proper as differing
word senses. There were a few incidents of homonymy that complicated the
alignment process for ATMONTO and AIRM-O. For example, the term “Flow”
had a slightly different meaning in ATMONTO and AIRM-O. In AIRM-O, a flow
is a traffic pattern, while in ATMONTO flow is a concrete measurement of the
number of aircraft per time unit traversing a volume of airspace.The classes have
an exact or close lexical match, but the two classes correspond to two different
word senses.
Modeling style mismatches are further decomposed into concept description
and paradigm mismatches. A concept description mismatch occurs when two
similar concepts are modelled differently, e.g., that the same intention is modelled
through the use of properties in one ontology and by using distinct sub-classes
for the same target values in the other ontology [6]. A specific type of concept
description mismatch between ATMONTO and AIRM-O is classes with similar
names defining different versions of the same concept based on differing technical
standards adopted by ontology developers, e.g., by FAA and EUROCONTROL.
Finally, paradigm mismatches refer to how different paradigms can be used to
represent concepts such as time, action, plans, causality, propositional attitudes,
etc. For example, one ontology might use temporal representations based on
interval logic, while another might use a representation based on points [6].
Paradigm mismatches relate to what we call “differing representation”, and one
example of such a mismatch is between P lannedF lightRoute in ATMONTO
8 A. Vennesland et al.
and T rajectory in AIRM-O. These two classes are used to represent the planned
aircraft trajectory (or flight plan). In AIRM-O, the planned trajectory is com-
posed of a sequence of trajectory points, elements, segments, and constraints. In
ATMONTO, the flight plan is specified using a hierarchically decomposable route
structure. These are fundamentally different methods of representing a planned
route, based on different conceptual models of what constitutes a route.
5 Performance of Automatic Matching Systems
We challenged three matching systems that normally rank highly on several
tracks of the OAEI campaigns on the equivalence reference alignment:
– AgreementMakerLight (AML) [9]. We ran AML using the GUI version
from 20168 and the “Automatic Match” mode, letting AML handle the
configuration of individual matching algorithms and external background
sources (e.g. WordNet). AML includes terminological, structural and lexical
matchers and uses WordNet as a general-purpose lexical resource as well
as the Doid and Uberon ontologies for matching of biomedical ontologies.
Property relations included in the produced alignment were disregarded when
evaluating the performance of AML.
– LogMap [11]. We used the latest available standalone distribution of LogMap9
with default matching parameters. LogMap combines terminological matching
with capabilities for diagnosing and repairing incoherent alignments. Option-
ally, LogMap can also employ external resources such as WordNet. As with
AML there were some property relations included in the produced alignment,
which we do not consider in the evaluation.
– YAM++ [16]. YAM++ is provided as a web application10 . We used the
default matcher parameters, which included both an element-level and a
structure-level matching algorithm.
The evaluation results from running the matching systems on the equivalence
reference alignment are shown in Figure 2. As the figure shows, all three systems
manage to avoid many false positives, especially LogMap which obtains perfect
precision with no false positives. All three systems obtain a recall of 0.31. The
results reveal that all three matching systems are able to correctly detect the true
positive relations where the source and target classes are exact string matches.
All three matchers also capture one relation where the source class (SID) is an
acronym of the target class (StandardInstrumentDeparture) due to the fact that
“Standard Instrument Departure” is expressed in the label of the source class. The
remaining relations in the reference alignment are not detected by these systems.
A closer inspection of the alignments produced by these three matching
systems with respect to the equivalence reference alignment reveals that the
following factors contribute to making this a challenging dataset:
8
There was an issue with the dependency to the Gephi Toolkit that prevented us from
using the most recent version of AML.
9
https://sourceforge.net/projects/logmap-matcher/files/
10
http://yamplusplus.lirmm.fr/index
Matching Ontologies for Air Traffic Management 9
– Domain-specific and technical terminology. Most of the classes in both on-
tologies describe aviation-specific concepts and technical terms. Often the
class names and their natural language definitions include acronyms and
abbreviations used only in aviation. Considering that typically used lexical re-
sources (such as the aforementioned WordNet) have low coverage of technical
terminology, this constitutes a challenge for matching systems.
– Compound class names. Several of the classes involved in the relations repre-
sented in the reference alignment contains equal substrings, a feature often
exploited by string-matching techniques. However, in most relations one or
both class names are compound words, such as PhysicalRunway - Runway or
AircraftModel - AircraftMakeModelSeries, resulting in a low similarity scores
for algorithms based on basic substring analysis. Here, a more comprehensive
string-based analysis is required to identify such relations, possibly result-
ing in the unwanted effect that additional false positive relations are being
included in the computed alignment as well.
– Synonymy, homonymy and polysemy. The two ontologies use synonymous
terms for concepts with the same meaning (e.g. Airport vs. Aerodrome).
Synonymy can often be resolved using lexicons or other external sources (e.g.
other ontologies). Homonymy and polysemy are more of a challenge to solve.
Some of the class names in these two ontologies can have a different meaning
outside the ATM domain. Examples of this are Gate, Taxi or Star (which
is short for Standard Terminal Arrival Route in the ATM world) and such
challenges are not addressed through the use of lexicons such as WordNet.
Evaluation of equivalence r eference alignment
1,00
0,90
0,80
0,70
0,60
0,50
0,40
0,30
0,20
0,10
0,00
AML LogMap YAM++
Precision Recall F-measure
Fig. 2. Performance of selected state-of-the-art matchers over ATMONTO and AIRM-O
10 A. Vennesland et al.
6 Related Work
Evaluation datasets that include reference alignments declaring the correct set of
mappings between ontologies are important for the continued improvement of
ontology matching techniques. The OAEI provides an annual standardised evalu-
ation process for matching system. However, with only a few exceptions over the
years, the OAEI tracks mainly involve one-to-one equivalence relations, neglecting
other semantic relations and complex correspondences whose identification is
important for more profound integration processes [8, 18]. One of these OAEI
tracks is the Conference Track, a widely used benchmark for ontology matching
systems, that since its inception in 2005 has been subject to many revisions [26].
This track now includes 16 ontologies describing conference organization and
there are two versions of reference alignments, all holding one-to-one equivalence
relations. The first version is referred to as “crisp” alignments where all confidence
values are 1.0. The second version is referred to as an “uncertain” version of the
reference alignment where the confidence values reflect the opinion from a group
of human experts [7].
For the 2018 OAEI campaign, a complex alignment track was launched,
offering reference alignments holding complex relations in four different datasets.
One of the datasets included complex reference alignments for some of the
ontologies in the Conference Track [19]. The other datasets represented real-world
ontologies from the domains of hydrography, plants and species, and geoscience.
Having real-world ontologies in benchmarks is important because such ontologies
may expose issues arising in practice which may be overlooked by the developers
of (semi-)artificial benchmarks [27].
7 Summary and Future Work
We contrasted AIRM-O with the ATMONTO. Mismatches between these ontolo-
gies coupled with the complex and diverse nature of the ATM domain, which
covers many technical subject fields, renders automatic ontology matching diffi-
cult. The presented manual alignment of AIRM-O and ATMONTO potentially
facilitates integration of datasets in different formats, e.g., NASA aeronautics
research data with ATM information in the operational System Wide Information
Management (SWIM) network. As a byproduct, the ontology matching commu-
nity gains access to a reference alignment for two complex real-world ontologies
from the ATM domain. We refer to a separate publication [10] for a more detailed
comparison of AIRM-O and ATMONTO from an ATM perspective.
Future work will investigate the potential for complex reference alignments
between AIRM-O and ATMONTO beyond simple equivalence and subsumption
relations. using the Expressive and Declarative Ontology Alignment Language
(EDOAL) [2]. During the manual mapping process, we identified a large number
of complex relations, e.g., class-to-property relations and many-to-many relations,
which additional reference alignments can be developed from. In this regard,
complex matching represents an area with a potential for significantly advancing
the state-of-the-art in ontology matching.
Matching Ontologies for Air Traffic Management 11
Acknowledgments. We thank Scott Wilson from EUROCONTROL and Joe
Gorman from SINTEF for their contributions to the reference alignment. Part
of this work was conducted as part of the BEST project. This project received
funding from the SESAR Joint Undertaking under grant agreement No 699298
under the European Union’s Horizon 2020 research and innovation program. This
work was also supported by the NASA Airspace Operations and Safety Program.
The views expressed in this paper are those of the authors.
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