Using a Random Subspace Predictor to Integrate Spatial and Temporal Information for Traffic Flow Forecasting

  • Shiliang Sun
  • Changshui Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3611)


Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems. Due to practical limitations, traffic flow records may be partially missing or substantially contaminated by noise. In this paper, a robust traffic flow predictor, termed random subspace predictor, is developed integrating the entire spatial and temporal information in a transportation network to cope with this case. Experimental results demonstrate the effectiveness and robustness of the random subspace predictor.


Gaussian Mixture Model Temporal Information Transportation Network Markov Chain Model Intelligent Transportation System 
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 2005

Authors and Affiliations

  • Shiliang Sun
    • 1
  • Changshui Zhang
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Department of AutomationTsinghua UniversityBeijingChina

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