Random Matrix Theory Analysis on the Spatial Correlation of Section Speed

It is of great practical significance to understand the structure and operation condition of urban road network. In the Random Matrix Theory (RMT) analysis, the authors calculate cross-correlation matrix C of road sections in Shanghai, and analyze the distribution of eigenvalues and their corresponding eigenvectors of matrix C. A remarkable agreement between theoretical prediction and empirical data concerning the distribution of eigenvalues is shown, while there are still a few eigenvalues of matrix C fall outside the bounds predicted by RMT, and the distribution of eigenvectors corresponding to these eigenvalues display deviations from RMT results. In addition, the deviating eigenvectors show stability in time. At last, some applications of RMT analysis in transportation are discussed.

• Record URL:
  https://doi.org/10.1061/41039(345)518
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784410394
• Supplemental Notes:
  • © 2009 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • He, Xiang
  • Yang, Dongyuan
  • Hui, Ying
• Conference:
  • Second International Conference on Transportation Engineering
  • Location: Chengdu , China
  • Date: 2009-7-25 to 2009-7-27
• Publication Date: 2009-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 3148-3153
• Monograph Title: International Conference on Transportation Engineering 2009

Subject/Index Terms

• TRT Terms: Eigenvalues; Eigenvectors; Spatial analysis; Trip matrices; Urban highways
• Geographic Terms: Shanghai (China)
• Subject Areas: Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01534528
• Record Type: Publication
• ISBN: 9780784410394
• Files: TRIS, ASCE
• Created Date: Nov 12 2013 1:43PM