Abstract
In this paper, a plane moving average algorithm is proposed for solving the urban road flow forecasting problem. This new approach assembles information from relevant traffic time series and has the following advantages: (1) it integrates both individual and similar flow patterns in making prediction, (2) the training data set does not need to be large, (3) it has more generalization capabilities in predicting unpredictable and much complex urban traffic flow than previously used methods. To assess the new model, we have performed extensive experiments on a real data set, and the results give evidence of its superiority over existing methods.
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Lv, L., Chen, M., Liu, Y., Yu, X. (2015). A Plane Moving Average Algorithm for Short-Term Traffic Flow Prediction. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_28
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DOI: https://doi.org/10.1007/978-3-319-18032-8_28
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