Fluctuation Scaling in the
Air Traffic Controller
Communication Activities

Yanjun Wang*▲, Minghua Hu▲, and Vu Duong☆
▲Nanjing University of Aeronautics and Astronautics*Telecom ParisTech☆ EUROCONTROL Experimental Centre

2nd ENRI International Workshop on ATM/CNS, Tokyo, Japan, 2010

Outline

1ATM Complex System Modeling2

The understanding of ATCO activities

3

Data and Methods

4

Initial Results

5

Validation and Future WorkATM Complex System Modeling

ATM as a Complex System?
Primary Characteristics of Complex Systems

1 Structural Complexity (combinatorial or detail complexity),ATMSystem✔

i.e. when system consists of a large number ofinterconnected parts

2 Behavioural Complexity (dynamic complexity), i.e. when✔

prediction of system outputs or emergent behaviour isdifficult.

3 Nested Complexity (multi-levels organizational✔

complexity), i.e. complex physical/technical systemembedded in a larger system. The two-way interactions
between adjacent levels create nested complexity.

4 Evaluative Complexity, e.g. multi-stakeholders
environment where good performance to one may not begood to another: difficult decision to make✔

Implications
Can’t readily predict emergent behavior
even if we understand the subsystems,e.g. ASAS in Controlled Airspace or DataLink in ATC.
Simplification doesn’t work – If focusing
on single sectors, the results may be
counterproductive, e.g. “Capacity” orworking methods in Super Sector Project.
System may be “policy” resistant, e.g.
harmonization of ATC systems.

Modeling Approach
Knowledge ofATM functions &structures

Conceptual
Principles for
Modeling
Structures(Resources &Processes)

Modeling
Framework(CLIOS)

Model ofATM
ComplexSystem

Theories
Representing
PhysicalPhenomena

Physics
Underlying System
Dynamics(HamiltonianMechanics)

Outline

1

ATM Complex System Modeling2

The understanding of ATCO activities

3

Data and Methods

4

Initial Results

5

Validation and Future Work

ATCO in the continuous environment
Input into ATCO:
Output of the physical system:
Flight plans;
Rapid changed traffic situation
Current traffic Situation;
which may How
resultdoes
in various
the ATCO manage all the resources?
Sector configuration;system performance, such as
Weather information;
safety and efficiency.
Etc

Input into physical system throughvoice comm/CPDLC:
ATC instructions

Output of the ATCO:
Control strategies in the form ofcombination of different instructionsHuman performance aspects, suchas workload.
The only ways for the information
flow into physical system

The understanding of ATCO
 Structure-based abstraction employed by the ATCO tosimply mental load (Histion & Hansman, 2008):
 Standard flow, Critical point, Grouping, andResponsibility

Universal laws govern human activities Since 2005, there have been more than 50 papers on humandynamics published in the renowned journals, such as
Nature, Science, PNAS, Physical Review Letters, etc.
 Investigations on the large empirical data sets show that

 The timing of many human activities can becharacterized by the heavy tailed feature withbursts of rapidly occurring events separated by thelong periods of inactivity.
 There exist similarities between activities patterns
among human beings, which are irrelevant to thecontext of the activities.

How about ATCO?

1. Temporal behavior ofcommunication
2. Spatial behavior
3. Relationships between
the aggregate
communication activities
and traffic amountUnknown route structureOutline

1

ATM Complex System Modeling2

The understanding of ATCO activities

3

Data and Methods

4

Initial Results

5Validation and Future Work

Data 1: ATCOSIM
 The data we analyzed is the ATCOSIM Air Traffic Control
Simulation Speech corpus of EUROCONTROL Experimental
Centre.
 ATCOSIM consists of ten hours communication data, whichwere recorded during ATC real-time simulations conducted
between 20/01/1997 and 14/02/1997.
 During the simulations, only the voices of controllers, not ofpilots, were recorded.
TOTAL (50)

AVERAGE

Length of the exercises ( hh:mm:ss )11:18:37

1:11:10

Number of the flights (complete flights*) identified in theexercise

3121 (1966)

63 (40)

Number of the communication events (Unidentified) in theexercise

10078 (1276)

202(26)

Data 2: TMA Paris EUROCONTROL has launched on 7 June a two weeks
real-time simulation to test the viability of improvements
proposed by the French DSNA to the air traffic systemserving Paris-Charles De Gaulle, Paris-Orly and Paris-Le
Bourget airports.
 There are 20 simulation exercises, and each exercise
includes more than 22 sectors.

Sectors simulated
Secteurs CRNA/N simulés et FEEDFN(également jusqu’auFL055 sous TP,
THLN partie Nordet TE)
TETP

EGA : Positions simulées
Creil

AP

LORTA

LORTA

VELER

MERUE

EGA

THLN

TML
BALOD

FE
(égalementjusqu’auFL055 sous TML)

DENPG

MERUE

INNPG
+ COOR

DESPG

AR

OGRT
AOUS

BALOD

INIPO
+ COORUJ

A 2010 - Simulation – 7 - 18 Juin 2010+ COOR

ITMPO
DEPPO
MOLEK

ODRAN

FS
(également jusqu’au FL055
sous AOUS/AR/UJ et DOGS)

IAFs: Positions appr

Transition Altitude

IAFs: Positions approximatives

OMAKO
KAMOL+ ASSFW(également jusqu’au FL055
sous OYOT, OGRTet THLN partie Sud)

INSPG

Villacoublay

DOGS
OYOT

ITBPG

+ COORITSPGMOLEKVELER+ COORKAMOL OMAKO

ODRAN

ITNPG

TA 2010
2010- Simulation –57 - 18 Juin 2010

Transition Altitude2010

Outline

1ATM Complex System Modeling2

The understanding of ATCO activities

3

Data and Methods

4

Initial Results

5

Validation and Future WorkInitial ResultsTemporal behavior: heavy tailed feature of
Fluctuation Scaling: Taylor’s lawTemporal behavior (ICRAT 2010)010

zf1 07
zf1 07
zf2 07
zf2 07sm1 07sm1 07sm2 07sm2 07
zf1 08zf1 08sm2 08sm2 0801010-1

10200

150100-210-250101100102101000

210105010t/seconds

100150200

250300350400

450-110Pr(X  x)

Pr(T  t)-1250-2

10-310010121010x

310Sector: DEPPO

010-1

Pr(X  x)

Pr(X  x)

-1

10-2

1010-210-3-3100

110210x
Sector: OGRT

010

121010310x
Sector: TP

010-1-1Pr(X  x)

10-2

1010-210-3100101010Pr(X  x)

Sector: AOUS

010-3010121010x

31010010121010x310Cumulative number of the communication events

EFS (Data 1)
180160140120100806040
200051015202530
Cumulative number of the flights

354045Taylor’s Power Law
 The Taylor’s power law is named after L. R. Taylor inrecognition of his paper in 1961.
 Taylor’s law can be applied to describe many
complex systems in characterizing the relationshipbetween the fluctuation in the activity of an element
and the average activity.
 The relationship is usually in the following from:a25STD

20y = 0.14*x + 0.7151050020406080100120140120140

Average of the number of the communication eventsb2
1.5
10.50-0.5-1-1.5-2020406080100

EFS (Data 2)
EFS in Sector: AOUS

EFS in Sector: AP

EFS in Sector: AR

EFS in Sector: CREIL

EFS in Sector: DENPG

400200200200

40020010010010020000204060
EFS in Sector: DEPPO

002040EFS in Sector: DESPG

002040EFS in Sector: INIPO

0051015EFS in Sector: INNPG

0400

40040040040020020020020020000204060
EFS in Sector: ITBPG

00204060
EFS in Sector: ITMPO

00204060
EFS in Sector: ITNPG

00204060
EFS in Sector: ITSPG

0400

200

400

40020020010020020010000102030
EFS in Sector: OYOT

001020EFS in Sector: TE30002040
EFS in Sector: THLN

0020
EFS in Sector: TML

400200400200400200100200100200

100001020EFS in Sector: UJ

30040010020050002040600002040
EFS in Sector: VILLA

10200020400020406000204060
EFS in Sector: INSPG

02040
EFS in Sector: OGRT

001020EFS in Sector: TP

10203030

454035STD

302520151050050

100150200
Average of the communication activities

250300Outline

1ATM Complex System Modeling

2The understanding of ATCO activities

3

Data and Methods

4

Initial Results

5

Validation and Future Work

Future work
1. Further analysis of the TMA Paris
simulation data.
2. Investigate the “spatial behavior” of theATCO activities.
3. Modeling the temporal and spatial
phenomena of the ATCO activities.

Acknowledgement

We would like to thank
EUROCONTROL for the support ofthis study.