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Detecting the Breakdown of Traffic

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Author Info

  • Xi Zou
  • David Levinson

    ()
    (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

Timely traffic prediction is important in advanced traffic management systems to make possible rapid and effective response by traffic control facilities. From the observations of traffic flow, the time series present repetitive or regular behavior over time that distinguishes time series analysis of traffic flow from classical statistics, which assumes independence over time. By taking advantage of tools in frequency domain analysis, this paper proposes a new criterion function that can detect the onset of congestion. It is found that the changing rate of the cross-correlation between density dynamics and flow rate determines traffic transferring from free flow phase to the congestion phase. A definition of traffic stability is proposed based on the criterion function. The new method suggests that an unreturnable transition will occur only if the changing rate of the cross-correlation exceeds a threshold. Based on real traffic data, detection of congestion is conducted in which the new scheme performs well compared to previous studies.

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File URL: http://nexus.umn.edu/Papers/Breakdown.pdf
File Function: First version, 2007
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Bibliographic Info

Paper provided by University of Minnesota: Nexus Research Group in its series Working Papers with number 000034.

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Date of creation: 2006
Date of revision:
Handle: RePEc:nex:wpaper:breakdown

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Postal: Dept. of Civil Engineering, 500 Pillsbury Drive SE, Minneapolis, MN 55455
Phone: +01 (612) 625-6354
Fax: +01 (612) 626-7750
Web page: http://nexus.umn.edu
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Related research

Keywords: Congestion; Queueing; Traffic Flow; Congestion Pricing;

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  1. Daganzo, C. F. & Cassidy, M. J. & Bertini, R. L., 1999. "Possible explanations of phase transitions in highway traffic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(5), pages 365-379, June.
  2. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
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