Analysis of Urban Traffic Patterns Using Clustering

This thesis reports on research undertaken to improve the understanding of variations in urban traffic levels. The author analyzed within and between day variations in traffic volumes, focusing on travel time reliability, the robustness of the road network, and dynamic traffic management. The traffic volume data used for the analyses was provided by urban traffic information centers. The author developed a quality control procedure that detects invalid daily records of volume measurements; the resulting validated traffic data is linked to data on factors that could cause variations in traffic volume, such as calendar data, weather, road work, events, and traffic accidents. The author analyzed temporal and spatial variations in urban traffic volumes using cluster analysis for both working and non-working days. The proposed framework was applied to traffic data from the city of Almelo, The Netherlands, an urban area of approximately 70,000 inhabitants. The author concludes that the analysis framework functions adequately. The cluster analyses resulted in distinctive, recurrent, and representative traffic patterns that can be explained by travel demand and supply factors. The obtained insight into existent traffic patterns can be used for traffic monitoring, forecasting, management, and transport modeling scenarios.

  • Availability:
  • Corporate Authors:

    Netherlands TRAIL Research School

    P.O. Box 5017
    Delft,   Netherlands  2600 GA
  • Authors:
    • Weijermars, Wendy A M
  • Publication Date: 2007


  • English

Media Info

  • Media Type: Print
  • Features: Figures; Maps; References; Tables;
  • Pagination: 213p

Subject/Index Terms

Filing Info

  • Accession Number: 01054678
  • Record Type: Publication
  • ISBN: 9789036524650
  • Files: TRIS
  • Created Date: Jul 30 2007 6:53PM