Machine Learning 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：

2020 FIFTH INTERNATIONAL CONFERENCE ON FOG AND MOBILE EDGE COMPUTING (FMEC) | 2020年

基金：

美国国家科学基金会;

关键词：

Interruption detection; loop detectors systems; machine learning; time series analysis;

D O I：

10.1109/fmec49853.2020.9144876

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Detection of traffic interruptions 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 using high resolution data from widely deployed inductive loop detectors. The techniques highlighted in this paper are tested on data collected from detectors installed on more than 300 signalized intersections over a 6 month period. Our results show that we can detect interruptions with high precision and recall.

引用

页码：231 / 236

页数：6

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