Data Mining Algorithms for Traffic Interruption Detection

Yashaswi Karnati, Dhruv Mahajan, Anand Rangarajan, Sanjay Ranka

Abstract

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.

Download


Paper Citation


in Harvard Style

Karnati Y., Mahajan D., Rangarajan A. and Ranka S. (2020). Data Mining Algorithms for Traffic Interruption Detection.In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-419-0, pages 106-114. DOI: 10.5220/0009422701060114


in Bibtex Style

@conference{vehits20,
author={Yashaswi Karnati and Dhruv Mahajan and Anand Rangarajan and Sanjay Ranka},
title={Data Mining Algorithms for Traffic Interruption Detection},
booktitle={Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2020},
pages={106-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009422701060114},
isbn={978-989-758-419-0},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Data Mining Algorithms for Traffic Interruption Detection
SN - 978-989-758-419-0
AU - Karnati Y.
AU - Mahajan D.
AU - Rangarajan A.
AU - Ranka S.
PY - 2020
SP - 106
EP - 114
DO - 10.5220/0009422701060114