The united states air transportation network analysis
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The United States air transportation network analysis. Dorothy Cheung. Introduction. The problem and its importance Missing Pieces Related works in summary Methodology Data set Network Generation Network Analysis Conclusion. Outline. The problem and its importance Missing Pieces

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Introduction
Introduction

  • The problem and its importance

  • Missing Pieces

  • Related works in summary

  • Methodology

    • Data set

    • Network Generation

    • Network Analysis

  • Conclusion


Outline
Outline

  • The problem and its importance

  • Missing Pieces

  • Related works

  • Methodology

    • Data set

    • Network Generation

    • Network Analysis

  • Conclusion


The problem and its importance
The problem and its importance

  • Problem

    • Analysis the air transportation network in the U.S.

      • Network driven by profits and politics

      • Better understand the network structure not maximize utility

  • Importance

    • Economy: transport of good and services

    • Air traffic flow: convenience

    • Health studies: propagation of diseases


Outline1
Outline

  • The problem and its importance

  • Missing Pieces

  • Related works

  • Methodology

    • Data set

    • Network Generation

    • Network Analysis

  • Conclusion


Missing pieces
Missing pieces

  • Sufficient amount of researches on the network with focuses on utility optimization.

  • Commercial enterprises: OAG and Innovata

  • But … lack of research on analyzing the network features studied in class.


Outline2
Outline

  • The problem and its importance

  • Missing Pieces

  • Related works

  • Methodology

    • Data set

    • Network Generation

    • Network Analysis

  • Conclusion


Related works air transportation networks analysis
Related worksAir transportation networks analysis

  • WAN – World-wide Airport Network

  • ANI – Airport Network of India

  • ANC – Airport Network of China


Related works summary features of air transportation networks
Related worksSummary: Features of air transportation networks

  • Small world network (compared with random graphs)

    • Small average shortest path

    • High average clustering coefficient

    • Degree mixing differs

  • Scale free power law degree distribution


Outline3
Outline

  • The problem and its importance

  • Missing Pieces

  • Related works

  • Methodology

    • Data set

    • Network Generation

    • Network Analysis

  • Conclusion


Methodology
Methodology

  • Data Set

  • Network Generation

  • Network Analysis


Methodology data set
Methodology – Data Set

T100

OAI

RITA

BTS

DATABASE

My data

Legends

OAI : Office of Airline Information

RITA : Research and Innovative Technology Administration

BTS : Bureau of Transportation Statistics


Methodology data set1
Methodology – Data Set

Domestic Air Traffic Hubs [1]


Methodology data set2
Methodology – Data Set

  • Domestic scheduled flights

    • Passengers, cargos, and mails

    • Military excluded

  • Market Data vs. Segment Data

    • Market : Used

      • Accounts for passenger once on the same flight number

    • Segment : Not used

      • Accounts for passenger more than once per leg

  • Month specific : July 2011


Methodology data set3
Methodology – Data Set

  • Relevant information

    • Number of Passengers

    • Number of Cargos : Freight and Mail

    • Origin City

    • Destination City

Sample .csv from BTS


Methodology network generation
Methodology – Network Generation

  • Network

    • 850 Nodes: airports

    • 21405 entries

      • Weighted edges: sum of passengers and cargos

    • Directed and Undirected network input files for Pajak [2] and GUESS [5].


Methodology network generation1
Methodology – Network Generation

.CSV

GenerateNwk

Microsoft.Jet.OLEDB4.0Provider

Network Generation Tool written in C# using LINQ (Language Integrated Query)

ParseCSV

Data Table

LINQ

PajekDirected.net

PajekUndirected.net

GUESSDirected.gdf

GUESSUndirected.gdf


Methodology network generation2
Methodology – Network Generation

The U.S. Air Transportation Network drawn in Pajek


Methodology network analysis
Methodology – Network Analysis

  • Metrics

    • Degree distributions and correlations

      • Top 10 most connected cities

      • Top 10 most central cites

    • Small world network?

      • Shortest path length

      • Clustering coefficient

      • Compare against WAN, ANI, and ANC

    • Cumulative degree distribution and the power law

    • Resilience

    • Associativity : Rich-club?

    • Random graph

    • Z-Score TBD?


Methodology network analysis1
Methodology – Network Analysis

  • Degree distributions and correlations

    • Directed network

    • Pajek:

      • In degree : Net -> Partitions -> Degree -> Input

      • Out degree : Net -> Partitions -> Degree -> Output

      • Both : Net -> Partitions -> Degree -> All

  • Shortest path length

    • Directed network

    • Pajek:

      • Net -> Paths between 2 vertices -> Diameter

  • Clustering coefficient

    • Directed network

    • Pajek:

      • Net -> Paths between 2 vertices -> Diameter


Methodology network analysis2
Methodology – Network Analysis

  • Cumulative degree distribution and the power law

    • Directed network

      Step 1 in Pajek:

      • Create a partition of all degree

      • Export the partition in a tab delimited file

      • Tools -> Export to Tab Delimited File -> Current Partition

        Step 2 in MatLab [6]:

      • Generating a power law integer distribution

        X = GetInput.m: reads the partition from the tab delimited file

        (X => X.name, X.label, X.degree)

      • Calculating the cumulative distribution

        cumulativecounts.m [4]

        [xlincumulative,ylincumulative] = cumulativecounts(X.degree)


Methodology network analysis3
Methodology – Network Analysis

  • Resilience

    What % of nodes are removed to reduce the size of the Giant component by half?

    • Consider:

      • Random attack

      • Targeted attack : remove nodes with the highest degree and betweenness centrality measures

    • Undirected network with 850 nodes

    • GUESS toolbars: resiliencedegree.py and resiliencebetweenness.py that are downloaded from cTools[4]

    • Compare against a random network (Random and targeted attacks)

      GUESS : makeSimpleRandom(numberOfNodes, numberOfEdges)

      => numberOfNodes = 850

      numberOfEdges = 21405


Methodology network analysis4
Methodology – Network Analysis

  • Associativity : Rich-club?

    • Draw conclusion from graphical analysis in GUESS

  • Random graph

    • Difficulty in constructing a realistic random network that models the real network [3].

  • Z-Score?

    • To Be Determined.


Methodology network analysis5
Methodology – Network Analysis

  • Expectations/Predictions

    • Larger degree nodes are more central (betweenness). Consider LAX, SFO, HOU, JFK, etc.

    • Small world as compared to

      WAN, ANI, and ANC

    • Scale free power law distribution

    • Dissociate


Outline4
Outline

  • The problem and its importance

  • Missing Pieces

  • Related works

  • Methodology

    • Data set

    • Network Generation

    • Network Analysis

  • Conclusion


Conclusion
Conclusion

The United States air transportation network analysis

  • The problem and its importance

  • Missing Pieces

  • Related works – WAN, ANI, ANC

  • Methodology

    • Data set : BTS : Bureau of Transportation Statistics

    • Network Generation : Directed and Undirected network input files

    • Network Analysis :

      • Degree distribution

      • Small world network as compared to WAN, ANI, and ANC

      • Cumulative degree distribution and power law

      • Resilience

      • Associativity

      • z-score – TBD?


References for this presentation
References for this presentation

  • T-100 reporting guide, RITA, http://www.rita.dot.gov/, www.transtats.bts.gov, http://www.bts.gov/programs/airline_information/.

  • Pajak, program for large network analysis, http://vlado.fmf.uni-lj.si/pub/networks/pajek/.

  • Albert-Laszlo Barabasi and Reka Albert, “Emergence of Scaling in Random Networks”, Department of Physics, University of Notre-Dame, October, 1999.

  • CTools, https://ctools.umich.edu/portal.

  • GUESS, graph exploration system, http://graphexploration.cond.org/.

  • Matlab, The language of technical computing, http://www.mathworks.com/products/matlab/index.html


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