[PPT] - Statistical Regularities in ATM: Network Properties, Trajectory PowerPoint Presentation

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Statistical Regularities in ATM: Network Properties, Trajectory Deviations, and Delays

SID 2012 – Braunschweig, 27th of November, 2012

S. Vitali, G. Gurtner, L. Valori , M. Cipolla, V. Beato, S.

Pozzi, S. Miccich` e, F. Lillo & R. Mantegna

ELSA

Empirically grounded agent based models for the future ATM scenario

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Presentation of ELSA

Empirically grounded agent based models for the future ATM scenario Deep Blue Valentina Beato Simone Pozzi Universit` a di Palermo Stefania Vitali Marco Cipolla Salvatore Miccich` e Rosario Mantegna Scuola Normale Superiore Luca Valori G´ erald Gurtner Fabrizio Lillo

G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 2 / 30

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Presentation of ELSA

Aim

Build an Agent-Based Model integrating many actors at different levels in

rder to test new scenarios in ATM.

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Presentation of ELSA

Aim

Build an Agent-Based Model integrating many actors at different levels in

rder 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.

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Data and Database

Data...

... on trajectories: M1 (last filled flight plan) and M3 (trajectories updated 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...).

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Outline

1

Airports Network Strength/degree distributions Network communities Dynamics

2

Sectors Network Dynamics Deviations

3

Navigation points Communities Deviations

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Airports

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Airports: Network

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Airports: Network

Properties of nodes

Degree: number of destination from/to the airport Strength: number of flights from/to the airport

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Airports: Network

Size proportional to degree.

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Airports: network metrics

10

1

10

2

10

3

10 10

1

10

2

PDF = 2.2 Airport Strength

10 10

1

10

2

10 10

1

10

2

PDF = 1.9 Airport Degree

Scale free network

Presence of hubs, very short path between any points in the network (≃ 3).

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Airports: companies

10 10

1

10

2

0.5 1

EDDM

Degree Btwn, DLH

10 10

1

10

2

0.5 1

EGSS

Degree Btwn, RYR

10 10

1

10

2

0.5 1

EKCH

Degree Btwn, SAS

10 10

1

10

2

0.5 1

LEPA

Degree Btwn, BER

10 10

1

10

2

0.5 1

LTBA

Degree Btwn, THY

10 10

1

10

2

0.5 1

EGLL

Degree Btwn, BAW

10 10

1

10

2

0.5 1

EGKK

Degree Btwn, EZY

10 10

1

10

2

0.5 1

LIRF

Degree Btwn, AZA

10 10

1

10

2

0.5 1

LFPG

Degree Btwn, AFR

Betweenness centrality

Measure of how much the node is central in the network ≃ number of shortest path passing through it.

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Airports: communities

General purpose/methods

Communities are group of nodes which are mutually more highly interconnected than they are with the rest of the network. Many algorithms have been proposed to partition a network in communities:

1

Maximization of modularity: finds the partition that maximizes modularity, which is the fraction of the links within the given communities minus the expected such fraction if links were distributed at random (under some null hypothesis);

2

Infomap: based on random walks on networks,

After a partition has been obtained, one can characterize each community by measuring the over-expression of a given node attribute.

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Airports: communities

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Airports: communities

Big supra national communities

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Airports: communities

Big supra national communities ⇒ tool to design airspaces?

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Airports: dynamics

Seasonality

Number of flights and active airports are changing on a daily basis, yearly basis and because of external shocks (volcano).

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Airports: dynamics

Monday 13/05/2010 Sunday 19/05/2010 Cluster

verexpressed

size Cluster

verexpressed

size attribute attribute 1 uk 121 1 norway 107 1 ireland 121 1 sweden 107 1 france 121 1 finland 107 2 norway 113 1 denmark 107 2 sweden 113 2 uk 104 2 finland 113 2 ireland 104 2 denmark 113 3 germany-civil 97 3 germany-civil 89 3 austria 97 3 poland 89 3 romania 97 3 austria 89 4 spain 51 4 greece 45 4 portugal 51 4 romania 39 5 turkey 40 5 spain 39 6 italy 36 5 portugal 39 7 greece 32 6 turkey 34 7 italy 27

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Sectors

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Sectors: structure

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Sectors: structure

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Sectors: network

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Sectors: strength and degree

Non scale-free network

typical degree, strength, length... big diameter.

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Sectors: dynamics

Structure of the network is changing during the day.

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Sectors: dynamics

Change of the traffic on the network, change of the underlying network (geographical neighbours of sectors).

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Sectors: deviations

The change in the structure allows to absorb the traffic without more reroutings.

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Navigation points

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Navpoints: map

Finer scale, geographical network.

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Navpoints: communities

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Navpoints: communities

Infomap

Big communities, looking like airspaces: ACC?

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Navpoints: communities

Infomap

Big communities, looking like airspaces: ACC? ⇒ tool to design airspaces?

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Navpoints: deviations 1

Local metrics

Number of flights deviated, point common in M1 (planned trajectory) and M3 (actual trajectory), area generated, etc...

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Navpoints: deviations 2

Number of horizontal deviations drops with local traffic. Number of vertical deviations increases with local traffic.

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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 traffic decreases Nodes of high degree gets flights more delayed when the traffic increases

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Conclusion

Airport Network

Scale-free network (small world), different type of network for companies,

rganized 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,

rganized in communities which look like the ACCs,

Deviations are handled differently at high degree nodes and low degree nodes.

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Thanks for your attention

Deep Blue Valentina Beato Simone Pozzi Universit` a di Palermo Stefania Vitali Marco Cipolla Salvatore Miccich` e Rosario Mantegna Scuola Normale Superiore Luca Valori G´ erald Gurtner Fabrizio Lillo

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