EUROCONTROL EXPERIMENTAL CENTRE1 / 29/06/2016  Raphaël CHRISTIEN  Network Capacity & Demand Management  5 th USA/Europe ATM 2003 R&D seminar  23 rd -27 th June 2003 ATC Complexity Indicators & ATC Sectors Classification

EUROCONTROL EXPERIMENTAL CENTRE2 / 29/06/2016 Outline  Introduction  Workload and Complexity  ATC complexity indicators  ATC sectors classification : methods  Results  Conclusions and future work

EUROCONTROL EXPERIMENTAL CENTRE3 / 29/06/2016 Introduction  Goal of an air traffic organisation : ensure safety while satisfying demand  The air traffic controller must not be overloaded  Demand cannot be always satisfied

EUROCONTROL EXPERIMENTAL CENTRE4 / 29/06/2016 Controller’s Work  2 groups of tasks  Single aircraft tasks  Coordination  Monitoring ……  Aircraft interaction tasks  Conflict search  Conflict resolution ……

EUROCONTROL EXPERIMENTAL CENTRE5 / 29/06/2016 Macroscopic Workload Model  Workload for single aircraft tasks  wlFL x FLs  wlFL : average workload for single aircraft tasks per flight  FLs : number of flights

EUROCONTROL EXPERIMENTAL CENTRE6 / 29/06/2016 Macroscopic Workload Model  Workload for interactions aircraft tasks  wlINT x INTs  wlINT : average workload for interactions aircraft tasks per interaction  INTs : number of interactions

EUROCONTROL EXPERIMENTAL CENTRE7 / 29/06/2016 Macroscopic Workload Model  Controller’s workload macroscopic evaluation  WL= wlFL x FLs + wlINT x INTs

EUROCONTROL EXPERIMENTAL CENTRE8 / 29/06/2016 Macroscopic Workload Model  wlFL and wlINT have to be split into 2 parts  Operational part (radio tasks,...)  Complexity part (lot of traffic mix,...)  wlFL= wlFLop + wlFLcplx  wlINT= wlINTop + wlINTcplx  The higher wlFLcplx and wlINTcplx are,  the higher impact of complexity is.

EUROCONTROL EXPERIMENTAL CENTRE9 / 29/06/2016 Impact of Complexity : Example  In a high complexity sector : workload increases faster with the number of flights Complexity is higher in the red sector than in the yellow one

EUROCONTROL EXPERIMENTAL CENTRE10 / 29/06/2016 Global Complexity Definition  A system is complex if  It contains more than one element AND  These elements interact together  ATC sector system example  If there is only one aircraft within the ATC sector  Few interactions between aircraft  Complexity is very low

EUROCONTROL EXPERIMENTAL CENTRE11 / 29/06/2016 Global Complexity Definition  Measuring the complexity of a system  The more parts the system contains  The more different interactions between its elements  The more complex the system is.

EUROCONTROL EXPERIMENTAL CENTRE12 / 29/06/2016 ATC Complexity Indicators

EUROCONTROL EXPERIMENTAL CENTRE13 / 29/06/2016 Sector Complexity : Evaluation  Evaluate each sectors complexity with  Single aircraft complexity indicators  Interactions complexity indicators  Validation by operational experts

EUROCONTROL EXPERIMENTAL CENTRE14 / 29/06/2016 Complexity Indicators : Single Aircraft  Number and type of flights by time period (entry, presence)  Amount of climbing/descending traffic  Military activity  Proximity of a centre boundary

EUROCONTROL EXPERIMENTAL CENTRE15 / 29/06/2016 Complexity Indicators : Interactions  Multiple crossing points  Number and type of conflicts  Small angle convergence routes  Separation standards  Aircraft performance mix (jets, props...)  Density

EUROCONTROL EXPERIMENTAL CENTRE16 / 29/06/2016 Complexity Indicators Analysis  Each sector is described by its list of complexity indicators :  (Fl, crossing,...)  Large amount of data

EUROCONTROL EXPERIMENTAL CENTRE17 / 29/06/2016 Complexity Indicators Analysis  Organise the data set  Extract and keep only interesting parts  Enable an unambiguous interpretation of complexity  Discover typical complexity structures : categorisation of sectors  Allow to target specific groups of sectors

EUROCONTROL EXPERIMENTAL CENTRE18 / 29/06/2016 ATC Sectors Classification

EUROCONTROL EXPERIMENTAL CENTRE19 / 29/06/2016 Classification  Classification :  Find a way to divide the sectors into homogeneous classes (categories)  These classes have to be different  A large problem : the number of possible outcomes to test is huge! For 25 elements, 4E18 possible combinations!  Need to use approximate methods

EUROCONTROL EXPERIMENTAL CENTRE20 / 29/06/2016 Divisive Classification  Unsupervised method  No previously defined classes  No predefined number of classes  Hierarchical method  Gives a binary tree structure on data  Tree enables easy interpretation  Divisive: tree built from root

EUROCONTROL EXPERIMENTAL CENTRE21 / 29/06/2016 Divisive Classification Method  Start with single root cluster representing all sectors  Split the root cluster into 2 leaf clusters  Recursively split each leaf cluster into 2 sub-clusters.  Stop when ‘stopping condition’ satisfied

EUROCONTROL EXPERIMENTAL CENTRE22 / 29/06/2016 A Binary Decision Tree (n=700) ROOT CLUSTER (n=300) FIRST LEAF (n=400) SECOND LEAF A B CD

EUROCONTROL EXPERIMENTAL CENTRE23 / 29/06/2016 DIVAF Method : Split Criterion  Each split is divided by one complexity indicator :  That shows a strong differential distribution within a group and serves to distinguish between different sub-groups.  We use principal component analysis (PCA) to detect such indicators.  If many indicators are detected, operational advice is needed

EUROCONTROL EXPERIMENTAL CENTRE24 / 29/06/2016 A DIVAF Binary Decision Tree (n=700) Conflicts Route length (n=400) Route Length Centre boundary Conflicts (n=300) Aircraft mix A BCD High Low Indicator chosen by expert

EUROCONTROL EXPERIMENTAL CENTRE25 / 29/06/2016 DIVAF PCA  Sectors are described in N dimensions with N the number of complexity indicators.  We reduce this dimension by PCA in 2D (projection that preserved distance best between sectors’ points)  If 2 sectors’ points are near together then they probably share similar complexity.

EUROCONTROL EXPERIMENTAL CENTRE26 / 29/06/2016 DIVAF PCA

EUROCONTROL EXPERIMENTAL CENTRE27 / 29/06/2016 DIVAF Stop  The DIVAF algorithm stops when  It does not find any complexity indicator strongly linked with PC1 for any cluster.  All clusters are small enough

EUROCONTROL EXPERIMENTAL CENTRE28 / 29/06/2016 Results : Experience  European traffic of 04/09/00  ATFM simulator  Statistical tools  Experts operational advice

EUROCONTROL EXPERIMENTAL CENTRE29 / 29/06/2016 Results : Geographical Location Low conflicts Mostly cruising flights Upper sectors Class 1

EUROCONTROL EXPERIMENTAL CENTRE30 / 29/06/2016 Results : Geographical Location Class 2 Low conflicts Performance mix

EUROCONTROL EXPERIMENTAL CENTRE31 / 29/06/2016 Results : Geographical Location Class 3 Lot of conflicts Medium climbing /descending traffic

EUROCONTROL EXPERIMENTAL CENTRE32 / 29/06/2016 Results : Geographical Location Class 4 Lot of conflicts Traffic mix Climbing/Descending

EUROCONTROL EXPERIMENTAL CENTRE33 / 29/06/2016 Using the Classes  We can improve some workload models by specialising them using the typology obtained previous step  We will compare model values to CFMU reference values

EUROCONTROL EXPERIMENTAL CENTRE34 / 29/06/2016 Conclusion  These methods can be used ‘as is’ with new indicators  They prove useful  Field of application is large  Improvement of workload models  Evaluation of future airspace designs ...