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Symmetry 2017, 9(5), 70; doi:10.3390/sym9050070

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

University of Science and Technology Beijing, Beijing 100083, China
Authors to whom correspondence should be addressed.
Academic Editor: Yuhua Luo
Received: 20 January 2017 / Revised: 2 May 2017 / Accepted: 4 May 2017 / Published: 10 May 2017
(This article belongs to the Special Issue Symmetry in Cooperative Applications II)
View Full-Text   |   Download PDF [7163 KB, uploaded 10 May 2017]   |  


There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition. View Full-Text
Keywords: traffic congestion evaluation; Kernel-SSELM; asymmetric data; cooperative learning traffic congestion evaluation; Kernel-SSELM; asymmetric data; cooperative learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shen, Q.; Ban, X.; Guo, C. Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine. Symmetry 2017, 9, 70.

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