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Characteristic Analysis for Regional Traffic Data Using Random Matrix Theory

  • Haichun LiuEmail author
  • Changchun Pan
  • Genke Yang
  • Chunxia Zhang
  • Robert C. Qiu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 340)

Abstract

Traffic regional feature analysis of city is an important problem in the study of macro transportation system. The analysis of the current traffic regional feature of the city is mainly through the floating car data, such as velocity’s average or variance to characterize the features of different regions. But with the increasing scale of the traffic data in sampling and storage, the average (or variance) velocity as the evaluation of city traffic regional feature such as distinguishing urban and suburb has no significant through our analysis. Therefore, in this paper, we use the Random Matrix Theory (RMT) to analysis traffic data, which is based on floating car position data’s distribution of singular value equivalence matrix eigenvalue to distinguish different region such as urban and suburban. By using 1 week’s floating car data, we verify the data’s eigenvalue of singular value equivalent matrix distribution is better than the velocity’s average or variance in indicating the regional feature of city traffic.

Keywords

RMT Big data Spectral analysis Data mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Haichun Liu
    • 1
    Email author
  • Changchun Pan
    • 1
  • Genke Yang
    • 1
  • Chunxia Zhang
    • 2
  • Robert C. Qiu
    • 3
  1. 1.Department of AutomationShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Wainmac Information Technology Co. LtdShanghaiChina
  3. 3.Department of Electrical and Computer EngineeringTennessee Technological UniversityKnoxvilleUSA

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