Clustering radar tracks to evaluate
efficiency indicators
Roland Winkler (roland.winkler@dlr.de), Annette Temme, Christoph Bösel,Rudolf Kruse
November 11, 2010

Outline

1Introduction

2

Clustering Flight Tracks

3

KPI: Idealized Flight Time2 / 21

Current Section

1

Introduction

2

Clustering Flight Tracks

3

KPI: Idealized Flight Time3 / 21

Key Performance Indicators for inbound traffic
The Airport Sequencing and Metering Area (ASMA)
contains the TMA and roughly the airspace needed forseparation of aircraft
The ASMA is realizes as simple as possible as a 100NM
radius circle around the ARP
Key Performance Indicators (KPIs) for measuring theperformance of handling inbound traffic
ASMA-flight time median
ASMA-flight time variancerelative variance andidealized flight time (virtual optimal flight time)

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Data base for this analysis
The data consists of a set of Airport locations and a setof flight tracks
Each flight track consists of a series of points which inturn hold several information
3D-Location
Time
Velocity
Staring Airport, Destination Airport
etc.

More than 1000 Airports involved, 65 of which with morethan 500 total approaches within time frame
NOT in Data base
Airport layout of any kind
TMA layout
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Data examples

typical flight tracks

flight tracks with errors

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Current Section

1Introduction

2Clustering Flight Tracks

3

KPI: Idealized Flight Time

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Motivation for Clustering
ASMA Flight time depends on the combination ofentrance direction and landing direction
Aircraft approaching an airport from different directions
or landing on different runways are not comparable
First task: find groups of aircraft that use similar routes(clustering)
For each approach cluster (group of routes of similarrouted aircraft), cluster dependent KPIs are calculated
The final KPI is calculated as a weighted sum of thecluster dependent KPIs with the fraction of total flights as
weightTwo different clustering approaches are proposed
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First approach: Clustering ASMA entrance andlanding locations
Two steps of clustering are required: ASMA-Entrance
Point and landing point
For the ASMA-entrance point clustering, the firstrecorded radar point inside the ASMA is used
Filter routes out where the first recorded radar point islonely or too far in the centre
Calculate the location of the aircraft at 6NM from theARP and use this position for a second clustering step
Each combination of entrance cluster and runway cluster
forms an approach cluster
Example: approach cluster 1 = [4, 1]
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Clustering entrance points

unclustered entrance points

clustered entrance points

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Modified Fuzzy c-Means for clustering
Clustering landing points and entrance points is very
similarEach approach route is assigned with a degree ofmembership for each cluster
Use the highest degree of membership to determine thecluster affiliation
Problem: number of clusters must be known in advancefor Fuzzy c-Means
Use a modified version of the algorithm that requires thediameter of the clusters instead

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Clustered flight tracks

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Second approach: Clustering flight tracks by flighttrack similarity
Define a distance (dissimilarity) function for flight tracks.
Possible simple distances:
Area in between the flight tracks
Maximal distance of flight tracks

Used Distance function: weighted point-wise distance toincrease the influence of flight track difference near theARPThe DBScan algorithm is used to cluster the aircraft withthe defined distance measure

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Data set, clustered by flight track similarity

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Comparing clustering methods
Noise data objects are data objects that seemingly
belong to no particular group
Noise clusters are quite different for both approaches
Removing noise data objects acts like a filter as thesedata objects are removed
Clustering by flight track similarity filters rare approach
routesClustering by entrance and landing points filters
infrequent used entrance points

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Noise data from clusteringentrance/landing point

flight track similarity

16 / 21Current Section

1Introduction

2Clustering Flight Tracks

3

KPI: Idealized Flight Time17 / 21

Idealized approach route and time
With the approach clusters, the KPIs Flight time median,variance and relative variance are easy to calculate
The KPI idealized flight time is more difficult
For each approach cluster, a fuzzy overflight frequency
map (OFM) is generated
For each OFM, an idealized approach route is calculates
It should be short, smooth, flyable and it should
represent the dataThe idealized approach route can be used for
simulations without knowing the exact airspace structure
The idealized flight time is calculated, using the idealized
approach route and the median of velocity
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Idealized approach route example

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Conclusions

Clustering provides insight into airspace usage
Different queueing strategies are analysable andcomparable
Clustering provides the tools to calculate the idealized
flight time
Results are usable for simulations and performancemonitoring

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Thank You for your attention

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