Kernel Semi-supervised Extreme Learning Machine Applied in Urban Traffic Congestion Evaluation

被引：2

作者：

Shen, Qing ^{[1
]}

Ban, Xiaojuan ^{[1
]}

Guo, Chong ^{[1
]}

Wang, Cong ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Beijing 100083, Peoples R China

来源：

COOPERATIVE DESIGN, VISUALIZATION, AND ENGINEERING, CDVE 2016 | 2016年 / 9929卷

关键词：

Kernel-SSELM; Cooperative learning; Traffic congestion evaluation;

D O I：

10.1007/978-3-319-46771-9_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：90 / 97

页数：8

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