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Hidden Order in Traffic Flows Using Approximate Entropy: An Illustration

  • Kingsley Haynes
  • Rajendra Kulkarni
  • Roger StoughEmail author
Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

The dynamic nature of traffic flows on urban freeways is self-evident. The plots of workday traffic on segments of major roads against time of day display the familiar contours of lumpy, peaked curves. Over the years the peaks have become blunt and the valleys filled, suggesting nearly day long high-volume traffic. At the same time that the average travel speed on congested freeways has gone up, average commute time has either remained steady or increased marginally and the number of accidents per 100 million VMTs has gone down or remained constant (Gordon et al. 1991; BTS 2006). Traffic at high volumes and high speeds or under designed roads should result in more accidents and slower travel times. This has not occurred but traffic has continued to increase. Congested traffic patterns suggest an inherent disorder or randomness. Could it be that there is a hidden order in the congested traffic patterns? It would be helpful to analyze and understand these linear spatial patterns to see the degree to which order/disorder associated with these patterns can be determined.

Keywords

Traffic Flow Traffic Pattern Congested Traffic Vehicle Detection Approximate Entropy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors appreciate the support of the NSF/EPA Grant #SES-9976483 “Social Vulnerability Analysis” and NSF Grant #ECS-0085981 “Road Transportation as a Complex Adaptive System” as well as the School of Public Policy’s USDOT Center of Excellence in Evaluation and Implementation funded under DOT Grant #DTRS98-G-0013. Any errors are the responsibility of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kingsley Haynes
    • 1
  • Rajendra Kulkarni
    • 2
  • Roger Stough
    • 3
    Email author
  1. 1.The School of Public PolicyGeorge Mason UniversityFairfaxUSA
  2. 2.Senior Research Analyst, School of Public PolicyGeorge Mason UniversityFairfaxUSA
  3. 3.Vice President for Research and Economic DevelopmentGeorge Mason UniversityFairfaxUSA

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