Abstract
Predicting traffic flow efficiently to encourage driver to avoid the sections which are going to have traffic jams is a good approach to deal with traffic congestion. Conventional prediction methods focused on specific information (e.g., speed, density, flux, and so on). However, they will consume a lot of time and storage space. This paper proposes a novel prediction approach by analyzing the aggregated information of data streams to avoid unnecessary time and storage consumption. Evaluation shows that compared with existing similar approach ES (Exponential Smoothing), this new approach can adjust its smoothing factor based on historical values and outperform in prediction results.
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Feng, J., Zhu, Z., Xu, R. (2012). A Traffic Flow Prediction Approach Based on Aggregated Information of Spatio-temporal Data Streams. In: Watanabe, T., Watada, J., Takahashi, N., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia: Systems and Services. Smart Innovation, Systems and Technologies, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29934-6_6
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DOI: https://doi.org/10.1007/978-3-642-29934-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29933-9
Online ISBN: 978-3-642-29934-6
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