Detection and classification of vehicles by measurement of road-pavement vibration and by means of supervised machine learning

Markus Stocker (Corresponding Author), Paula Silvonen, Mauno Rönkkö, Mikko Kolehmainen

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)

Abstract

Road vehicle detection and, to a lesser extent, classification have received considerable attention, in particular for the purpose of traffic monitoring by transportation authorities. A multitude of sensors and systems have been developed to assist people in traffic monitoring. Camera-based systems have enjoyed wide adoption over the last decade, partially substituting for more traditional techniques. Methods based on road-pavement vibration are not as common as camera-based systems. However, vibration sensors may be of interest when sensors must be out of sight and insensitive to environmental conditions, such as fog. We present and discuss our work on detection and classification of vehicles by measurement of road-pavement vibration and by means of supervised machine learning. We describe the entire processing chain from sensor data acquisition to vehicle classification and discuss our results for the task of vehicle detection and the task of vehicle classification separately. Using data for a single vibration sensor, our results show a performance ranging between 94% and near 100% for the detection task (1340 samples) and between 43% and 86% for the classification task (experiment specific, between 454 and 1243 samples).
Original languageEnglish
Pages (from-to)125-137
JournalJournal of Intelligent Transportation Systems: Technology, Planning, and Operations
Volume20
Issue number2
DOIs
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed

Keywords

  • digital signal processing
  • machine learning
  • road vehicle detection and classification
  • vibration sensors

Fingerprint Dive into the research topics of 'Detection and classification of vehicles by measurement of road-pavement vibration and by means of supervised machine learning'. Together they form a unique fingerprint.

  • Cite this