An Adaptive Smoothing Method for Traffic State Identification from Incomplete Information

  • Martin Treiber
  • Dirk Helbing
Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 32)


We present a new method to obtain spatio-temporal information from aggregated data of stationary traffic detectors, the “adaptive smoothing method”. In essential, a nonlinear spatio-temporal lowpass filter is applied to the input detector data. This filter exploits the fact that, in congested traffic, perturbations travel upstream at a constant speed, while in free traffic, information propagates downstream. As a result, one obtains velocity, flow, or other traffic variables as smooth functions of space and time. Applications include traffic-state visualization, reconstruction of traffic situations from incomplete information, fast identification of traffic breakdowns (e.g., in incident detection), and experimental verification of traffic models.

We apply the adaptive smoothing method to observed congestion patterns on several German freeways. It manages to make sense out of data where conventional visualization techniques fail. By ignoring up to 65% of the detectors and applying the method to the reduced data set, we show that the results are robust. The method works well if the distances between neighbouring detector cross sections do not exceed 3 km.


Traffic Flow Congested Traffic Smoothing Method Traffic Model Temporal Smoothing 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Martin Treiber
    • 1
  • Dirk Helbing
    • 1
  1. 1.Institute for Economics and Traffic, Faculty of Traffic Sciences “Friedrich List”Dresden University of TechnologyDresdenGermany

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