Abstract
Traffic flow condition prediction is a basic problem in the transportation field. It is challenging to play out full potential of temporally-related information and overcome the problem of data sparsity existed in the traffic flow prediction. In this paper, we propose a novel urban traffic prediction mechanism namely C-Sense consisting of two parts: CRF-based temporal feature learning and sequence segments matching. CRF-based temporal feature learning exploits a linear-chain condition random field (CRF) to explore the temporal transformation rule in the traffic flow state sequence with supplementary environmental resources. Sequence segments matching is utilized to match the obtained state sequence segments with historical condition to get the ultimate prediction results. Experiments are evaluated based on datasets obtained in Wuhan and the results show that our mechanism can achieve good performance, which prove that it is a potential approach in transportation field.
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Acknowledgements
This work was partially supported by National Key Basic Research Program of China “973 Project” (Grant No. 2011CB707106), Development Program of China “863 Project” (Grant No. 2013AA122301), National Natural Science Foundation of China “NSFC” (Grant No. 61103220, 61303212) and the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1278).
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Niu, X., Zhu, Y., Cao, Q., Zhao, L., Xie, W. (2015). A Novel Urban Traffic Prediction Mechanism for Smart City Using Learning Approach. In: Sun, L., Ma, H., Fang, D., Niu, J., Wang, W. (eds) Advances in Wireless Sensor Networks. CWSN 2014. Communications in Computer and Information Science, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46981-1_52
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DOI: https://doi.org/10.1007/978-3-662-46981-1_52
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