Characterization of traffic and structure in the U.S. airport network

Vineet Mehta, Feanil Patel, Yan Glina, Matthew Schmidt, Ben Miller, Nadya Bliss

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In this paper we seek to characterize traffic in the U.S. air transportation system, and to subsequently develop improved models of traffic demand. We model the air traffic within the U.S. national airspace system as dynamic weighted network. We employ techniques advanced by work in complex networks over the past several years in characterizing the structure and dynamics of the U.S. airport network. We show that the airport network is more dynamic over successive days than has been previously reported. The network has some properties that appear stationary over time, while others exhibit a high degree of variation. We characterize the network and its dynamics using structural measures such as degree distributions and clustering coefficients. We employ spectral analysis to show that dominant eigenvectors of the network are nearly stationary with time. We use this observation to suggest how low dimensional models of traffic demand in the airport network can be fashioned.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012
Pages124-129
Number of pages6
DOIs
StatePublished - Dec 1 2012
Externally publishedYes
Event2012 Conference on Intelligent Data Understanding, CIDU 2012 - Boulder, CO, United States
Duration: Oct 24 2012Oct 26 2012

Publication series

NameProceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012

Other

Other2012 Conference on Intelligent Data Understanding, CIDU 2012
CountryUnited States
CityBoulder, CO
Period10/24/1210/26/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Fingerprint Dive into the research topics of 'Characterization of traffic and structure in the U.S. airport network'. Together they form a unique fingerprint.

Cite this