Abstract
In urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can be used to unite manual observed data and extensively collected data and cooperatively build connection between congestion condition and road information. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness. In this paper, Kernel-SSELM model is used to train the traffic congestion evaluation framework, with both small-scale labeled data and large-scale unlabeled data. Both the experiment and the real-time application show the evaluation system can precisely reflect the traffic condition.
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Acknowledgement
This work was supported by National Nature Science Foundation of P. R. China (No. 61272357, 61300074).
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Shen, Q., Ban, X., Guo, C., Wang, C. (2016). Kernel Semi-supervised Extreme Learning Machine Applied in Urban Traffic Congestion Evaluation. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2016. Lecture Notes in Computer Science(), vol 9929. Springer, Cham. https://doi.org/10.1007/978-3-319-46771-9_12
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DOI: https://doi.org/10.1007/978-3-319-46771-9_12
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