Using a random subspace predictor to integrate spatial and temporal information for traffic flow forecasting

Authors:
Shiliang Sun;Changshui Zhang
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

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.