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.
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Sun, S., Zhang, C. (2005). Using a Random Subspace Predictor to Integrate Spatial and Temporal Information for Traffic Flow Forecasting. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_93
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DOI: https://doi.org/10.1007/11539117_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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