| Title | A Novel Urban Traffic Prediction Mechanism for Smart City Using Learning Approach |
|---|---|
| ID_Doc | 38439 |
| Authors | Niu, XG; Zhu, Y; Cao, QQ; Zhao, L; Xie, W |
| Title | A Novel Urban Traffic Prediction Mechanism for Smart City Using Learning Approach |
| Year | 2015 |
| Published | |
| 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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