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The United States air transportation network analysis

The United States air transportation network analysis

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The United States air transportation network analysis

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  1. The United States air transportation network analysis Dorothy Cheung

  2. Introduction • The problem and its importance • Missing Pieces • Related works in summary • Methodology • Data set • Network Generation • Network Analysis • Conclusion

  3. Outline • The problem and its importance • Missing Pieces • Related works • Methodology • Data set • Network Generation • Network Analysis • Conclusion

  4. 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

  5. Outline • The problem and its importance • Missing Pieces • Related works • Methodology • Data set • Network Generation • Network Analysis • Conclusion

  6. 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.

  7. Outline • The problem and its importance • Missing Pieces • Related works • Methodology • Data set • Network Generation • Network Analysis • Conclusion

  8. Related worksAir transportation networks analysis • WAN – World-wide Airport Network • ANI – Airport Network of India • ANC – Airport Network of China

  9. 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

  10. Outline • The problem and its importance • Missing Pieces • Related works • Methodology • Data set • Network Generation • Network Analysis • Conclusion

  11. Methodology • Data Set • Network Generation • Network Analysis

  12. 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

  13. Methodology – Data Set Domestic Air Traffic Hubs [1]

  14. 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

  15. Methodology – Data Set • Relevant information • Number of Passengers • Number of Cargos : Freight and Mail • Origin City • Destination City Sample .csv from BTS

  16. 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].

  17. 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

  18. Methodology – Network Generation The U.S. Air Transportation Network drawn in Pajek

  19. 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?

  20. 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

  21. 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)

  22. 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

  23. 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.

  24. 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

  25. Outline • The problem and its importance • Missing Pieces • Related works • Methodology • Data set • Network Generation • Network Analysis • Conclusion

  26. 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?

  27. 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