Abstract were happened to shut down for some reason

Abstract

The
purpose of the project is to analyze the connectivity of different airports in USA
and how important are they in US airport network within and the overall world. For
our analysis we used the data from US Bureau of Transportation Statistics from
Jan 2017 to May 2017 this dataset contains all flight routes. We used various
network measures like Centrality, Hub-Scores, Authority-Scores, PageRank to
predict the importance of the Airports in US domestic airport network. Also
tried to measure impact on other airports if some major airports were happened
to shut down for some reason like terrorist threats, bad weather or worker
strikes. Finally, we also have some not so prominent airports being the key to
attain a structural balance in the US Airport networks.

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Introduction

The
number of airline passengers are increasing YOY drastically. In 2016, there are
823 million passengers an all-time high with 719.0M Domestic travelers and 103.9M
International travelers. Out of these 85% of the airline passengers travel
through approximately 10% of the airports. This gives us the base that there
are some airports that needs to have high attention based on the current increase
in the airline travelers. We wanted to test is the number of passengers
traveling is the only measure to know the importance of an Airport or should we
also check into other facts like how connected is the airport with other
domestic or international airports. We started exploring data and found some
interesting facts that are presented in this report.

 

 

 

Data-Set

 The Airport data is taken from US Bureau of
transportation statistics from Jan’2017 to May’2017. The data consists of 4
main variables with 2,610 nodes and 64,204.

1.     
Origin Airport

2.     
Destination Airport

3.     
Number of Passengers

4.     
Distance Between Airports

The data contains all the
flight routes between 2 airports. Each Airport is a node and the edge starts
from the origin airport and ends at destination airport. The edges here
represent the flight routes from origin airport to destination airport. The
edges are weighted edges and weights are based on number of passengers
traveling in that route. Edge distance is proportional to the distance between
2 airports.

 

 

 

 

 

 

 

 

 

 

 

 

Top 5 Airports based on Hub-Score

Rank

Airport

Score

1

O’Hare
International-Illinois

0.0237

2

Hartsfield-Jackson-Georgia

0.0201

3

Denver-Colorado

0.0196

4

Philadelphia International-Pennsylvania

0.0178

5

Detroit
Metro-Michigan

0.0171

 

Top 5 Airports based on Authority-Score

Rank

Airport

Score

1

O’Hare
International-Illinois

0.0235

2

Hartsfield-Jackson-Georgia

0.0209

3

Denver-Colorado

0.0193

4

Philadelphia International-Pennsylvania

0.0184

5

Charlotte
Douglas-North Carolina

0.0172

 

 

 

 

 

 

Top 5 Airports based on PageRank

Rank

Airport

Score

1

Denver-Colorado

0.0174

2

Ted Stevens-Alaska

0.0169

3

O’Hare
International-Illinois

0.0151

4

Hartsfield-Jackson-Georgia

0.0147

5

Minneapolis-Minnesota

0.0129

 

Top 5 Airports based on Closeness
Centrality

Rank

Airport

Score

1

Denver-Colorado

0.4848

2

Ted Stevens-Alaska

0.4838

3

Memphis-Tennessee

0.4815

4

Minneapolis-Minnesota

0.4813

5

Hartsfield-Jackson-Georgia

0.4806

 

 

 

 

 

 

Top 5 Airports based on Betweenness
Centrality

Rank

Airport

Score

1

Ted
Stevens-Alaska

0.3538

2

Seattle-Washington

0.0873

3

Denver-Colorado

0.0757

4

Fairbanks-Alaska

0.0684

5

Minneapolis-Minnesota

0.0456