Download presentation

Presentation is loading. Please wait.

Published byCaitlin Barnicle Modified over 3 years ago

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 works Air transportation networks analysis WAN – World-wide Airport Network ANI – Airport Network of India ANC – Airport Network of China

9
Related works Summary: 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 WANANIANC Avg. shortest path4.442.067 Avg. Clustering Coef.0.620.65740.733 Degree mixingAssociativeDissociative WANANIANC Power law exponent 1.02.2 +/- 0.11.65

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 Legends OAI : Office of Airline Information RITA : Research and Innovative Technology Administration BTS : Bureau of Transportation Statistics T100 OAI RITA BTS DATABASE My data

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 PASSENGE RSFREIGHTMAILORIGIN_CITY_NAMEDEST_CITY_NAME DEST_CITY _NUM DEST_STAT E_ABR DEST_STAT E_FIPS DEST_STAT E_NMDEST_WACYEARQUARTERMONTH DISTANCE_ GROUPCLASS 5970017Akhiok, AKKodiak, AK1017AK2Alaska12011371F 192002Akhiok, AKKodiak, AK1017AK2Alaska12011371L 2400Akhiok, AKKodiak, AK1017AK2Alaska12011371F 200Akiachak, AKAkiak, AK1024AK2Alaska12011371F 176477482250Adak Island, AKAnchorage, AK1029AK2Alaska12011373F 2000Adak Island, AKAnchorage, AK1029AK2Alaska12011373L 10528320Akiachak, AKBethel, AK1055AK2Alaska12011371F 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 Microsoft.Jet.OLEDB 4.0Provider ParseCSV GenerateNwk Data Table.CSV PajekDirected.net PajekUndirected.net GUESSDirected.gdf GUESSUndirected.gdf LINQ Network Generation Tool written in C# using LINQ (Language Integrated Query)

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
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 1.T-100 reporting guide, RITA, http://www.rita.dot.gov/, www.transtats.bts.gov, http://www.bts.gov/programs/airline_information/.http://www.rita.dot.gov/www.transtats.bts.gov http://www.bts.gov/programs/airline_information/ 2.Pajak, program for large network analysis, http://vlado.fmf.uni- lj.si/pub/networks/pajek/.http://vlado.fmf.uni- lj.si/pub/networks/pajek/ 3.Albert-Laszlo Barabasi and Reka Albert, “Emergence of Scaling in Random Networks”, Department of Physics, University of Notre-Dame, October, 1999. 4.CTools, https://ctools.umich.edu/portal.https://ctools.umich.edu/portal 5.GUESS, graph exploration system, http://graphexploration.cond.org/.http://graphexploration.cond.org/ 6.Matlab, The language of technical computing, http://www.mathworks.com/products/matlab/index.html http://www.mathworks.com/products/matlab/index.html

Similar presentations

OK

How is this going to make us 100K Applications of Graph Theory.

How is this going to make us 100K Applications of Graph Theory.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To ensure the functioning of the site, we use **cookies**. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy & Terms.
Your consent to our cookies if you continue to use this website.

Ads by Google

Ppt on power sharing in democracy your vote Ppt on soft skills for bpo Download ppt on water scarcity Ppt on history of olympics locations Ppt on bluetooth communications Ppt on high voltage engineering fundamentals Ppt on different kinds of birds Ppt on power amplifier Converter pub to ppt online maker Air pressure for kids ppt on batteries