Algorithm Research for Freeway Incident Detection Based on SVM

被引：0

作者：

Zheng Changjiang ^{[1
]}

Jiang Yubo ^{[2
]}

Yu Zhangxiao ^{[1
]}

Lu Weijie ^{[1
]}

机构：

[1] Hohai Univ, Coll Civil & Transportat Engn, Nanjing 210098, Jiangsu, Peoples R China

[2] Anhui Prov Commun Survey Designinst, Hefei 230011, Anhui, Peoples R China

来源：

ROAD MATERIALS AND NEW INNOVATIONS IN PAVEMENT ENGINEERING | 2011年 / 223期

关键词：

Freeway; Incident Detection; Support Vector Machine; Fuzzy Cut Set; Reduced Training Set;

D O I：

暂无

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Consider that floating cars and fixed coil detectors have complementary advantages and the merit of Support Vector Machines technology, this paper uses Support Vector Machines technology at first to fuse two kinds of data, and make freeway incidents detecting. Secondly, combining with the characteristics of support vector, the freeway incident detection algorithm based on reduced training set and support vector machines is designed to use fuzzy cut set theory. The method reduces not only training samples but also training time and improves the practical application ability of incident detection algorithm based on maintaining the detection rate and false alarming rate.

引用

页码：196 / 205

页数：10