Statistical Regularities in ATM: Network Properties, Trajectory Deviations, and Delays SID 2012 – Braunschweig, 27 th 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
Presentation of ELSA Empirically grounded agent based models for the future ATM scenario Universit` Scuola Normale a di Superiore Palermo Deep Blue Luca Valori Stefania Vitali Valentina Beato G´ erald Gurtner Marco Cipolla Simone Pozzi Fabrizio Lillo Salvatore Miccich` e Rosario Mantegna G´ erald 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 in order to test new scenarios in ATM. G´ erald 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 in order 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. G´ erald 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 (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...). G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 4 / 30
Outline Airports 1 Network Strength/degree distributions Network communities Dynamics Sectors 2 Network Dynamics Deviations Navigation points 3 Communities Deviations G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 5 / 30
Airports G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 6 / 30
Airports: Network G´ erald 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 G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 7 / 30
Airports: Network Size proportional to degree. G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 8 / 30
Airports: network metrics 2 10 2 10 � = 2.2 � = 1.9 PDF PDF 1 10 1 10 0 0 10 10 1 2 3 0 1 2 10 10 10 10 10 10 Airport Strength Airport Degree Scale free network Presence of hubs, very short path between any points in the network ( ≃ 3). G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 9 / 30
Airports: companies 1 1 1 Btwn, DLH Btwn, RYR Btwn, SAS EDDM EGSS EKCH 0.5 0.5 0.5 0 0 0 0 1 2 0 1 2 0 1 2 10 10 10 10 10 10 10 10 10 Degree Degree Degree 1 1 1 Btwn, BAW Btwn, BER Btwn, THY LEPA LTBA EGLL 0.5 0.5 0.5 0 0 0 0 1 2 0 1 2 0 1 2 10 10 10 10 10 10 10 10 10 Degree Degree Degree 1 1 1 Btwn, EZY Btwn, AZA Btwn, AFR EGKK LIRF LFPG 0.5 0.5 0.5 0 0 0 0 1 2 0 1 2 0 1 2 10 10 10 10 10 10 10 10 10 Degree Degree Degree Betweenness centrality Measure of how much the node is central in the network ≃ number of shortest path passing through it. G´ erald 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 highly interconnected than they are with the rest of the network. Many algorithms have been proposed to partition a network in communities: Maximization of modularity: finds the partition that maximizes 1 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); Infomap: based on random walks on networks, 2 After a partition has been obtained, one can characterize each community by measuring the over-expression of a given node attribute. G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 11 / 30
Airports: communities G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 12 / 30
Airports: communities Big supra national communities G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 12 / 30
Airports: communities Big supra national communities ⇒ tool to design airspaces? G´ erald 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, yearly basis and because of external shocks (volcano). G´ erald 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 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 G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 14 / 30
Sectors G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 15 / 30
Sectors: structure G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 16 / 30
Sectors: structure G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 17 / 30
Sectors: network G´ erald 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. G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 19 / 30
Sectors: dynamics Structure of the network is changing during the day. G´ erald 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 of sectors). G´ erald 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 more reroutings. G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 22 / 30
Navigation points G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 23 / 30
Navpoints: map Finer scale, geographical network. G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 24 / 30
Navpoints: communities G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 25 / 30
Navpoints: communities Infomap Big communities, looking like airspaces: ACC? G´ erald 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? G´ erald 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... G´ erald 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. G´ erald 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 traffic decreases Nodes of high degree gets flights more delayed when the traffic increases G´ erald Gurtner et al. (ELSA) Statistical Regularities in ATM Braunschweig, Nov. 2012 28 / 30
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