Statistical Regularities in ATM: NetworkProperties, Trajectory Deviations, and Delays
SID 2012 – Braunschweig, 27th of November, 2012
S. Vitali, G. Gurtner, L. Valori , M. Cipolla, V. Beato, S.Pozzi, S. Micciche, F. Lillo & R. Mantegna
ELSAEmpirically grounded agent based models for the future ATM scenario
Presentation of ELSA
Empirically grounded agent based models for the future ATM scenario
Deep BlueValentina Beato
Simone Pozzi
Universita diPalermo
Stefania VitaliMarco Cipolla
Salvatore MiccicheRosario Mantegna
Scuola NormaleSuperioreLuca Valori
Gerald GurtnerFabrizio Lillo
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 2 / 30
Presentation of ELSA
Aim
Build an Agent-Based Model integrating many actors at different levels inorder to test new scenarios in ATM.
Steps
Extract statistical regularities and stylized facts from traffic data,
build the ABM,
use regularities for calibrating and validating the future ABM.
Here we present a selection of empirical results extracted from the data.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 3 / 30
Presentation of ELSA
Aim
Build an Agent-Based Model integrating many actors at different levels inorder to test new scenarios in ATM.
Steps
Extract statistical regularities and stylized facts from traffic data,
build the ABM,
use regularities for calibrating and validating the future ABM.
Here we present a selection of empirical results extracted from the data.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 3 / 30
Data and Database
Data...
... on trajectories: M1 (last filled flight plan) and M3 (trajectoriesupdated by radar track) files containing sequences of navpoints,
... on the structure of Airspace (NEVAC files): sectors, routes, etc,
... for 16 AIRAC cycles ' 1 year and three months.
Database...
... eliminating redundancies,
... allowing very fast query on huge amount of data,
... building data of higher level (measure of complexity...).
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 4 / 30
Outline
1 AirportsNetworkStrength/degree distributionsNetwork communitiesDynamics
2 SectorsNetworkDynamicsDeviations
3 Navigation pointsCommunitiesDeviations
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 5 / 30
Airports
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 6 / 30
Airports: Network
Properties of nodes
Degree: number of destination from/to the airport
Strength: number of flights from/to the airport
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 7 / 30
Airports: Network
Properties of nodes
Degree: number of destination from/to the airport
Strength: number of flights from/to the airport
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 7 / 30
Airports: Network
Size proportional to degree.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 8 / 30
Airports: network metrics
101 102 103100
101
102
= 2.2
Airport Strength100 101 102100
101
102
= 1.9
Airport Degree
Scale free network
Presence of hubs,
very short path between any points in the network (' 3).
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 9 / 30
Airports: companies
100 101 1020
0.5
1EDDM
Degree Bt
wn,
DLH
100 101 1020
0.5
1EGSS
Degree
Btw
n, R
YR
100 101 1020
0.5
1EKCH
Degree
Btw
n, S
AS
100 101 1020
0.5
1LEPA
Degree
Btw
n, B
ER
100 101 1020
0.5
1LTBA
Degree
Btw
n, T
HY
100 101 1020
0.5
1EGLL
Degree
Btw
n, B
AW
100 101 1020
0.5
1EGKK
Degree
Btw
n, E
ZY
100 101 1020
0.5
1LIRF
Degree
Btw
n, A
ZA
100 101 1020
0.5
1LFPG
Degree
Btw
n, A
FRBetweenness centrality
Measure of how much the node is central in the network ' number of shortestpath passing through it.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 10 / 30
Airports: communities
General purpose/methods
Communities are group of nodes which are mutually more highlyinterconnected than they are with the rest of the network.
Many algorithms have been proposed to partition a network incommunities:
1 Maximization of modularity: finds the partition that maximizesmodularity, which is the fraction of the links within the givencommunities minus the expected such fraction if links were distributedat random (under some null hypothesis);
2 Infomap: based on random walks on networks,
After a partition has been obtained, one can characterize eachcommunity by measuring the over-expression of a given nodeattribute.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 11 / 30
Airports: communities
Big supra national communities
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 12 / 30
Airports: communities
Big supra national communities
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 12 / 30
Airports: communities
Big supra national communities ⇒ tool to design airspaces?
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 12 / 30
Airports: dynamics
Seasonality
Number of flights and active airports are changing on a daily basis, yearlybasis and because of external shocks (volcano).
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 13 / 30
Airports: dynamics
Monday 13/05/2010 Sunday 19/05/2010
Cluster overexpressed size Cluster overexpressed sizeattribute attribute
1 uk 121 1 norway 1071 ireland 121 1 sweden 1071 france 121 1 finland 1072 norway 113 1 denmark 1072 sweden 113 2 uk 1042 finland 113 2 ireland 1042 denmark 113 3 germany-civil 973 germany-civil 89 3 austria 973 poland 89 3 romania 973 austria 89 4 spain 514 greece 45 4 portugal 514 romania 39 5 turkey 405 spain 39 6 italy 365 portugal 39 7 greece 326 turkey 347 italy 27
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 14 / 30
Sectors
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 15 / 30
Sectors: structure
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 16 / 30
Sectors: structure
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 17 / 30
Sectors: network
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 18 / 30
Sectors: strength and degree
Non scale-free network
typical degree, strength, length...
big diameter.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 19 / 30
Sectors: dynamics
Structure of the network is changing during the day.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 20 / 30
Sectors: dynamics
Change of the traffic on the network,
change of the underlying network (geographical neighbours ofsectors).
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 21 / 30
Sectors: deviations
The change in the structure allows to absorb the traffic without morereroutings.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 22 / 30
Navigation points
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 23 / 30
Navpoints: map
Finer scale, geographical network.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 24 / 30
Navpoints: communities
Infomap
Big communities, looking like airspaces: ACC?
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 25 / 30
Navpoints: communities
Infomap
Big communities, looking like airspaces: ACC?
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 25 / 30
Navpoints: communities
Infomap
Big communities, looking like airspaces: ACC? ⇒ tool to design airspaces?
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 25 / 30
Navpoints: deviations 1
Local metrics
Number of flights deviated,
point common in M1 (planned trajectory) and M3 (actual trajectory),
area generated,
etc...
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 26 / 30
Navpoints: deviations 2
Number of horizontal deviations drops with local traffic.
Number of vertical deviations increases with local traffic.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 27 / 30
Navpoints: deviations 3
Nodes of low degree are more avoided when the traffic increases
Nodes of high degree are more avoided when the traffic decreases
Nodes of low degree gets flights more delayed when the trafficdecreases
Nodes of high degree gets flights more delayed when the trafficincreases
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 28 / 30
Conclusion
Airport Network
Scale-free network (small world),
different type of network for companies,
organized in communities which look like the FABs.
Sector Network
Geographical network,
dynamical structure on top of dynamical conditions (traffic).
Navpoint Network
Finer scale, geographical network,
organized in communities which look like the ACCs,
Deviations are handled differently at high degree nodes and lowdegree nodes.
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 29 / 30
Thanks for your attention
Deep BlueValentina Beato
Simone Pozzi
Universita diPalermo
Stefania VitaliMarco Cipolla
Salvatore MiccicheRosario Mantegna
Scuola NormaleSuperioreLuca Valori
Gerald GurtnerFabrizio Lillo
Gerald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 30 / 30