Data Mining Algorithms for Traffic Interruption Detection

被引：0

作者：

Karnati, Yashaswi ^{[1
]}

Mahajan, Dhruv ^{[1
]}

Rangarajan, Anand ^{[1
]}

Ranka, Sanjay ^{[1
]}

机构：

[1] Univ Florida, Dept Comp & Informat Sci & Engn, Gainesville, FL 32611 USA

来源：

PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS (VEHITS) | 2020年

关键词：

Incident Detection; Loop Detectors Systems; Traffic Interruptions; Semi-Supervised; Data Mining;

D O I：

10.5220/0009422701060114

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

Detection of traffic interruptions (caused by vehicular breakdowns, road accidents etc.) is a critical aspect of managing traffic on urban road networks. This work outlines a semi-supervised strategy to automatically detect traffic interruptions occurring on arteries in urban road networks using high resolution data from widely deployed fixed point sensors (inductive loop detectors). The techniques highlighted in this paper are tested on data collected from detectors installed on more than 300 signalized intersections.

引用

页码：106 / 114

页数：9

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