Terminal Airspace Modelling for Unmanned AircraftSystems Integration

Aaron McFadyen1 and Terry Martin2

Abstract— This paper considers the problem of integratingunmanned aircraft into low altitude airspace above urbanenvironments, including major terminal areas and helicopterlanding sites. A simple set of data-driven modelling techniquesare used to explore, visualise and assess existing air traffic in amanner more informative to the unmanned aircraft community.First, low altitude air traffic data sets (position reports) areanalysed with respect to existing exclusion/no-fly zones. Second,an alternative geometric approach to defining and comparingvarious exclusion zones is derived based on set theory. Theanalysis is applied to a region of south-east Queensland,Australia including Brisbane International Airport and threehelicopter landing areas. The results challenge some of thecurrent unmanned aircraft regulations, and should help tomotivate a more rigorous scientific approach to safely integrateunmanned aircraft.

I. INTRODUCTION

Unmanned Aircraft Systems (UAS) constitute one ofthe fastest growing sectors of aviation in many countries.Unmanned aircraft offer an alternate or new solution to alarge number of tasks within a diverse set of commercialapplications from remote sensing in disaster response andagriculture, to infrastructure inspection in mining and realestate [1]. As such, there is an increased demand to allowunmanned aircraft regular access to both regional and urbanairspace, including in and around major cities.

Integrating unmanned aircraft into an urban airspace envi-ronment is difficult. Firstly, most major cities are co-locatedwith a major international airport, general aviation/trainingaerodromes, and helicopter landing sites (heliports) for me-dia, emergency services and police operations. This meansthere is a large volume of diverse air traffic (fixed androtary wing) extending from surface level. Secondly, thereis generally an increased population density as more peoplemigrate to major trade centres. This means that there aremore people located below an already crowded airspace filledwith low-level operations. Collectivity, this raises consid-erable safety concerns when considering the integration ofunmanned aircraft, particularly regarding collision risk. Byfurther increasing the volume and diversity of air traffic, therisk of collision (mid-air or ground) increases, along withthe potential for aviation related fatalities.

As a result, many regulatory bodies have stipulated restric-tions on unmanned aircraft operations within urban airspace.One of the hazards considered is public safety, so unmannedflights are confined to operate at a minimum proximity to

1,2 are with the Science & Engineering Faculty, Queensland University ofTechnology, Brisbane, Australia. aaron.mcfadyen@qut.edu.au

Fig. 1. Example airspace classifications in south-east Queensland, Aus-tralia. The image shows some of the military airspace (�), training airspace(civil and military) (�), controlled airspace boundaries (−) and a declareddanger area due to unmanned aircraft operations (�).

persons not involved in the operation. This means operat-ing unmanned aircraft for many media events, or aroundsports and music venues, is either prohibited or requiresspecial exemptions. Another hazard relates to the expectedair traffic density directly, such that unmanned operationsmay be restricted around key locations or geographic regions.For example, many countries have established predefinedexclusion or no-fly zones around aerodromes and helicopterlanding sites. Access to these exclusion zones depends onwhether the aerodrome is towered, and the type of unmannedoperators certificate held.

In defining exclusion zones, there is often minimal or notransparency in how such regions are determined. From anaviation safety regulators perspective, it would be reason-able to establish very conservative exclusion zones. Froman unmanned aircraft operators perspective, it would bedesirable to have a minimal and liberal set of exclusionzones. It is difficult to satisfy both stakeholders given theirfundamental motivations. Either there is missed commercialopportunity through overly strict regulations that do notsignificantly reduce safety, or there is more opportunity (andtherefore more operators) under relaxed regulations whichcould compromise system safety. In light of these concerns,the contributions of this paper are:� A spatial analysis of low-level urban air traffic near a ma-

jor international airport and emergency service helicopterlanding sites. The airspace region encompasses parts ofBrisbane, Australia.

� A data-driven and risk-inspired approach to determiningunmanned aircraft exclusion (no-fly) zones near terminalareas and helicopter landing sites.

This paper is structured as follows. Section II provides aliterature review of useful terminal air traffic modelling pa-rameters and approaches in the context of unmanned aircraftintegration. Section III details the air traffic data, includingdata mining (extraction) methods, data limitations and a dataanalysis with respect to current exclusion zone regulations.Section IV explores an alternate data-driven approach todefining unmanned aircraft exclusion zones. Conclusions andfurther work are offered in Section V.

II. BACKGROUND

Although aviation related fatalities have been decliningover the years, operations in and around terminal areasremains hazardous [2]. Recent data shows that over halfthe recorded aviation fatalities occur during the take-off,initial climb, approach and landing phases of flight [3].Given the increase in conventional air traffic and escalatedcommercial pressure to operate unmanned aircraft in urbanareas, maintaining a high level of aviation system safety inthe terminal area will remain a priority.

To this end, most regulators have taken a conservativeapproach and established a set of exclusion or no-fly zones1

for unmanned aircraft around aerodromes and heliports. Akey component in defining any such exclusion zoning is theprovision of appropriate air traffic modelling and analysistechniques. Such techniques should also include robust risk-centric metrics, so that the merits of various exclusion zoneproposals can be examined. The overall goal is to then shrinkthe exclusion zone boundaries without violating any TargetedLevel of Safety (e.g. 1.5 x 10−8 to 5 x 10−9 fatal accidentsper system flight hour.).

Modelling terminal air traffic has been tackled for manyyears. The diversity in modelling approaches often stemsfrom the intended application of the derived models. Simplis-tic models do not consider the air traffic directly, and insteadrely on the airspace classification and approach/departureprocedures to give an indication of the expected traffic typeand behaviour in each region. An example of this type ofmodel is shown in Fig. 1, and is often used to help flightplanning for unmanned aircraft operations.

More rigorous approaches attempt to model the risk ofcollision in a predefined volume as a function of expectedaircraft behaviour. The variation in pairwise aircraft trajec-tories (often within air routes) is used to derive a conflictprobability which acts as a proxy for risk. Several establishedcollision risk models have been used [4], [5] with moreadvanced approaches emerging [6], [7]. Although useful,many of these models cannot be applied to terminal airspace[8], given the underlying assumptions relating to aircraftbehaviour (constant velocity trajectories etc.).

In an attempt to capture a more global view of theaircraft traffic behaviour in terminal regions, data-drivenmodelling techniques have recently emerged. Using realtrack data (radar, ADS-B etc.), modelling is often tackled

1Unmanned aircraft may only operate within these exclusion zones withspecial permission.

from a flow monitoring or outlier detection perspective. Forthese applications, the goal is to define common patternsor traffic tubes, such that the approach and departure pathvariability can be captured and used to discriminate betweennormal and abnormal behaviours in real time. For example,terminal airspace models have been derived using geometricprimitives [9], Fourier analysis [10], Principle ComponentAnalysis [11], [12] and sequence alignment techniques [13]with varying success. In some cases, very accurate andcompact representations of major terminal air traffic routescan obtained, but often at the cost of discarding less fre-quent (but still important) flight tracks. From an exclusionzone modelling perspective, this can be problematic if thediscarded flight tracks are not sufficiently rare. Interestingly,despite the availability and importance of track data aroundurban areas, limited effort has been directed at developingrobust exclusion zone models from the data. Ideally, anysuch model should support the comparison between differentboundary choices in terms of collision risk, exclusion zonearea and ease of implementation.

What is clear in the literature, is that there is no consensuson the appropriate modelling technique to define sensibleterminal area exclusion zones. In this initial paper, the goalis to then explore different exclusion zone boundaries usingsome simple modelling techniques and comparison metrics.Based on the above findings, this paper proposes:� A data-driven geometric approach to analysing air traffic

movement near terminal areas and helicopter landingsites, that focuses on determining useful parameters todefine suitable unmanned aircraft exclusion zones.

� A simple set of metrics to discriminate between candi-date unmanned aircraft exclusion zones. The metrics areaimed at quantifying the simplicity of the exclusion zoneshape, as well as capturing the trade-off between reducingcollision risk and optimising usable airspace.

III. AIR TRAFFIC DATA

A. Data Source

The air traffic data is taken from real position reportsrecorded by the Australian Advanced Air Traffic System(TAAATS) used for current Air Traffic Management inAustralia. The data is first filtered to isolate all recordedflights in south east Queensland over the summer of 2014-2015 (December-February). This region of airspace is aninteresting case study as it includes the capital city ofBrisbane and its associated international airport, two majorgeneral aviation and training aerodromes, a military airport,and a number of emergency service helicopter landing sites.

Using aircraft identification tags and report timing, theposition reports are then condensed into an ordered set offlights or tracks. All flights with less than 3 position reportsare then removed, before each flight trajectory is re-sampledto provide 50 equally spaced data points. As a result thedata consists of 64,307 flights with 16,292,100 unique datapoints.

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Fig. 2. Example summer air traffic in Brisbane terminal area at or below500 feet above ground. The region shows all position reports (•) within5nm of the aerodrome reference point (ARP), both runways, a runway-based exclusion zone (−) and the take-off/departure splays (−/−−).

B. Terminal Areas and Helicopter Landing Sites

To reduce the data coverage and retain focus on theanalysis of exclusion zones, the data set is further reducedby partitioning it into two distinct regions. The first regionincludes the terminal region under 500 feet and extending5nm from the aerodrome reference point associated withBrisbane international airport. This region contains 95,498position related data points. The second region includes theairspace around three helicopter landing sites under 500 feet,and extending 3nm from each reference heliport (helipad).This region contains 3,618 position related data points.

The terminal region traffic is depicted in Fig. 2 alongwith some current examples of exclusion zones. The leastrestrictive zone is defined by a radial disc extending 3nmfrom the aerodrome reference point. A more conservative,and perhaps realistic zone, is defined by a closed contour(polygon) that has no boundary point less than 3nm fromeach runway threshold or aerodrome boundary (fence). Whenusing the runway thresholds, this zone can be created bygenerating 3nm radial discs from each threshold and tracinga boundary around the overlapping polygons. The averagespeed of the traffic in the region is approximately 120kts,with peak traffic during the daylight hours and early eveningsof each day (see Appendix).

The heliport region traffic is depicted in Fig. 3 alongwith an example exclusion zone. The zone is determinedusing the same approach as the runway-based exclusion zonefor terminal regions by replacing the runway threshold witheach heliport. Of note, individual 3nm radial exclusion zoneswould have to be considered if Brisbane’s hospitals weremore sparsely distributed. The average speed of the traffic inthe region is less than 50kts, with a consistent flow of trafficthroughout each day. Reduced traffic is also observed earlyin the morning.

For both airspace regions, these simple visualisationsillustrate that there are a number of regions with very little

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Example Heliport Exclusion Zones and Traffic

Fig. 3. Example summer air traffic around three Brisbane helicopter landingsites at or below 500 feet above ground. The region shows all positionreports (•) and a 3nm radial exclusion zone around each helipad (−−).The combined exclusion zones made up from the closed contour formed bythe boundary of each radial exclusion is also shown (−).

traffic. Given the duration of the data set, this can mean asingle flight over 3 months in some cases. As such, someof the existing exclusion zones may be overly conservative,reducing the opportunities for unmanned aircraft to utiliselow-density airspace around urban areas. With this in mind,the following section proposes an alternative data-drivenapproach to explore candidate exclusion zones in urban areas.

Remarks: The size of each aircraft or helicopter is notconsidered in this analysis, and all point visualisations ofthe actual aircraft are therefore not to scale. Additionally,some position reports in each region do not have an altitudedata point. These reports have been retained to consider theworst case scenario.

IV. DATA-DRIVEN EXCLUSION ZONES

The data-driven exclusion zones for each landing area arederived using some basic techniques common to set theoryanalysis. The basic approach is to derive candidate exclusionzones by considering compact convex and bounding (non-convex) polygons enclosing different collections of positionreports contained around each landing site. More relaxed(tighter) exclusion zones are then derived by successivelyremoving position reports and recalculating the polygons.

A. Notation

Each position report is denoted by a single point x(x, y),where x and y are the latitude and longitude coordinatesof the point. The convex hull conv(S) of a finite point setS ∈ R2 is the set of all convex combinations of its points.The bounding polygon ∂S of a finite point set S ∈ R2 isthe set of points in the closure of S , not belonging to theinterior of S .

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Fig. 4. Example data-driven terminal area exclusion zones for 100%, 99%and 98% (−/•,−/•,−/•) of the original air traffic data contained withina 5nm radius of the aerodrome reference point (ARP). The dashed andsolid lines define the convex and bounding polygons around the associatedposition reports. The minimum Hausdorff distance between the convex andnon-convex polygons is also depicted −/•.

B. Terminal Areas

For the terminal area, position reports x under 500 feetand within 5nm of the aerodrome reference point a areconsidered as the reference data set A0 such thatA0 = {xn ∈ R2 : ||xn−a||2 ≤ 5κ}, n = {1, ..., N} (1)

where N = |A0| is the number of position reports andκ defines the conversion constant from degrees to nauticalmiles. The first set of candidate exclusion zones is found bycalculating the convex hull CA0 and bounding polygon C̄A0of the reference data such that

CA0 = conv(A0), C̄A0 = ∂A0 (2)Tighter or less restrictive exclusion zones can then be calcu-lated by successively removing a percentage of the outermost(most distant) position reports from the reference data set.To find the outermost points, a distance metric d̂ is definedsuch that for each point n in the dataset

d̂n = minv∈V

(||xn − vi||2) n = {1, ..., N} (3)

where v defines a vertex (edge) from the set of vertices Vof the convex hull enclosing the terminal runway thresholds.

The points corresponding to the largest distances can thenbe removed to create smaller data sets A1, ...,Ak, wherethe index of the data set corresponds to the percentageof points removed from the reference data set A0. Bythen re-applying (2), the convex CA1 , ..., CAk and boundingpolygons C̄A1 , ..., C̄Ak can then be found. The result is a setof candidate exclusion zones that eventually contract to theconvex hull defined by the runway thresholds as k increases(see Fig. 4).

Remarks: Informally, the convex hull can be defined byplacing a taut rubber band around the entire set S . Convexhulls are found using the Quickhull algorithm [14]. The

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Fig. 5. Example data-driven helicopter landing site exclusion zonesfor 100%, 95% and 90% (−/•,−/•,−/•) of the original air traffic datacontained within a 3nm radius of the helicopter landing sites. The dashedand solid lines define the convex and bounding polygons around theassociated position reports. The minimum Hausdorff distance between theconvex and non-convex polygons is also depicted −/•.

bounding polygon is the set of outermost points in the setthat when joined using straight line segments (edges) encloseall points in the set. The set is typically non-convex, but canbe convex for certain data sets.

C. Helicopter Landing Sites

For the helicopter landing sites, position reports x under500 feet and within 3nm of any of the helicopter landingpads h ∈ {h1,h2,h3} are considered as the reference dataset H0 such that

H0 = {xm ∈ R2 : ||xm − h||2 ≤ 3κ}, m = {1, ...,M},(4)

where M = |H0| is the number of position reports. Similarto the case for the terminal area, the first set of candidateexclusion zones is found by calculating the convex hull CH0and bounding polygon C̄H0 of the reference data such that

CH0 = conv(H0), C̄H0 = ∂H0, (5)

Again, less restrictive exclusion zones can then be calculatedby successively removing a percentage of the outermostposition reports from the reference data set. To find theoutermost points in this case, the same distance metric d̂

d̂m = minz∈Z

(||xm − zi||2) m = {1, ...,M} (6)

is used where z now defines a line from the set of linesZ joining each helicopter landing zone reference point(helipad). Using (2) again, a set of candidate exclusion zonesdefined by the convex CH1 , ..., CHk and bounding polygonsC̄A1 , ..., C̄Hk can then be found. For helicopter landing areashowever, the exclusion zones eventually contract to theconvex hull defined by the helicopter landing site referencepoints as k increases (see Fig. 5).

Fig. 6. Candidate data-driven exclusion zones (�,�,�) for both terminalC̄Ak and heliport C̄Hk regions around Brisbane. Increasing k is depictedfrom blue to red. Currently enforced exclusion zones are also depicted (�).

D. Practical Application

Applying the data-driven approach to exploring candidateexclusion zones reveals some interesting results. For both theterminal and heliport regions, less restrictive exclusion zonescould be adopted, without removing any of the original datapoints. Therefore, the collision risk may not be effected byexpanding the operational envelope for unmanned aircraft inthe Brisbane region. However, this may reduce the separationbetween aircraft, which may not be palatable to the regulator.

After removing some of the data points from each region,the candidate exclusion zones start to take a more complexshape with a reduced area. Of note, large regions becomeaccessible to the west and east of the terminal region, andnorth and south of the heliport region.

However, care must be taken in interpreting these results.As data has been removed there are aircraft present outsideeach exclusion zone, so it is unclear how the collision riskhas been affected. A possible way to view the results is tothen consider a relative risk-opportunity metric R such that

Rk = |1− āk/N̄k| (7)where āk is the normalised area of the exclusion zone with kpercent of the data points removed, and N̄k is the normalisednumber of enclosed position reports. The area and numberof position reports are normalised using the original data setand associated region area (3nm polygon for heliports and5nm radial disc for terminal region). As the risk-opportunitymetric value increases, there are more aircraft per unit surfacearea in the candidate exclusion zone. Relatively speaking,this means that there may be more opportunity (usable area)without a significant increase in collision risk. The variationin Rk between the candidate exclusion zones defined by thebounding polygons is depicted in Fig. 7. Similar plots couldbe constructed for an arbitrary zone to visualise the trade-offbetween increasing the operational envelope and maintainingan acceptable risk of collision.

An alternate and softer approach to assessing the utilityof the data-driven exclusion zones is to consider their shape.This is important from an implementation perspective, asexclusion zones should be easy to understand and map (seeFig. 6). A metric that can be used to measure how far two

Percentage Removed (%)0 5 10 15

Risk-O

pportunity

0

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Fig. 7. Example risk-opportunity curve for bounding polygon exclusionzones for both terminal C̄A (◦) and heliport C̄H (�) regions. The riskopportunity for the original data set and exclusion zone is also included.

Percentage Removed (%)0 1 2 3 4 5 6 7 8 9 10

Hau

sdorffDistance

(dH)

0.015

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Fig. 8. Example Hausdorff distance variation between each convex C(·)and bounding C̄(·) exclusion zone pair for both terminal (◦) and helicopterlanding areas (�).

subsets of a metric space are from each other is the Hausdorffdistance dH defined as

dH(C, C̄) = max{supx∈C

infx̄∈C̄

d(x, x̄), supx̄∈Ĉ

infx∈C

d(x, x̄)} (8)

where x ∈ C, x̄ ∈ C̄ and d(x, x̄) = ||x− x̄||2. Applied to thedata-driven exclusion zones, dH = 0 if and only if C and C̄have the same closure. That is, the union of the points in eachexclusion zone and its associated boundary are the same.This means that a small Hausdorff distance suggests theshape of the convex and bounding exclusion zones are verysimilar. This is desirable from an implementation standpoint,as the closer the exclusion is to a convex shape, the morestraight forward the implementation and enforcement islikely to be. For the terminal and helicopter landing areas,the convex and bounding exclusion zones converge as thenumber of data points are removed. This makes sense as thetraffic density increases whilst the track shape become lessdiverse with reduced outliers (see Fig. 8).

V. CONCLUSIONS

Determining adequate exclusion zones for unmanned air-craft in an urban environment subject to frequent conven-tional air traffic is an important task. Existing regulationscurrently limit where unmanned aircraft may operate, but insome cases these can be overly strict without significantly re-ducing the risk of collision. This paper presented an alternatedata-driven geometric approach to defining exclusion zones,where each zone can be tailored to each unique environmentbased on the risk appetite of the regulating body. To thisend, a number of simple metrics were also proposed to helpvisualise the trade-off between increasing usable airspace andmaintaining an acceptable level of collision risk.

This work constitutes a particularly unique contributiontoward the integration of unmanned aircraft into an urban en-vironment, and provides a good foundation in which to stemfurther research and development. Extended research willexplore the inclusion of more comprehensive collision riskmetrics that explicitly consider aircraft speed and frequencyto compare and refine different exclusion zone boundaries.Alternatively, the affect of placing additional buffer zonesor separation boundaries on the data-driven exclusion zonescould be studied. Collectively, it is hoped that advancingthis work will provide a sensible and realistic framework inwhich to optimise the placement and structure of exclusionzones for unmanned aircraft.

APPENDIX I

Example aircraft speed and report time (hours) histogramfor 3 months of aircraft position reports taken from a regionof south east Queensland, Australia.

Fig. 9. Example speed distribution of traffic contained within the runway-based exclusion zone around Brisbane terminal (�) and heliport-basedexclusion zone around helicopter landing sites (�).

ACKNOWLEDGEMENTS

The authors would like to thank the Australian ResearchCentre for Aerospace Automation (ARCAA) at Queens-land University of Technology, the Queensland Governmentthrough the Department of Science, Information Technology,Innovation and the Arts (DSITI), Thales Australia and the

Fig. 10. Example time distribution of traffic contained within the runway-based exclusion zone around Brisbane terminal (�) and heliport-basedexclusion zone around helicopter landing sites (�).

Civil Aviation Safety Authority (CASA) for the funding tosupport this work. The authors also acknowledge AirservicesAustralia for supplying the air traffic data.

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