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Single- and Multi-Sensor Techniques to Improve Accuracy of Urban Vehicle Classification

Unkyu Park, Fabio Silva, Mengzhao Hou, John Heidemann, Genevive Guiliano, Xi Wang, and Nikhil Prashar
USC/Information Sciences Institute

Abstract

 Vehicle traffic sensors are an essential part of urban traffic management systems. However, humans are still widely used when short-term traffic classification is required, because current traffic data collection systems do not meet requirements for high accuracy, rapid deployment, and low cost. This paper explores the use of a sensor network for vehicle traffic classification. We accomplish low cost and easy deployment by using simple embedded computers and inductive loop sensors that can be taped directly to the roadway. We explore several techniques to improve accuracy both at individual sensors and by combining readings from multiple sensors. Traditional approaches typically assume high-speed traffic; we instead focus on errors that are induced at low and varying speeds, since we expect sensors to often be deployed at locations where vehicles will slow or stop. The contributions of this paper are first, a comprehensive evaluation of approaches to reduce error: from feature definition, combining readings from a single sensor, and combining results from multiple independent sensors. Second, we evaluate these approaches against both on-line and off-line human observations, demonstrating sensor accuracy better than or slightly worse than on-line human classification depending on the similarity of the categories, and nearly optimal for length-based classification.

Availability

This paper is available in several formats: abstract web page with pointers and cites, PDF, paper copies can be obtained by mail to the authors. Copyright terms for this paper appear below.

Reference

Park06a
Unkyu Park, Fabio Silva, Mengzhao Hou, John Heidemann, Genevive Guiliano, Xi Wang, and Nikhil Prashar. Single- and Multi-Sensor Techniques to Improve Accuracy of Urban Vehicle Classification. Technical Report ISI-TR-2006-614, USC/Information Sciences Institute, April, 2006. <http://www.isi.edu/~johnh/PAPERS/Park06a.html>. 
@techreport{Park06a,
	author = "Unkyu Park and Fabio Silva and Mengzhao Hou and John Heidemann and Genevive Guiliano and Xi Wang and Nikhil Prashar",
	title = "Single- and Multi-Sensor Techniques to Improve Accuracy of Urban Vehicle Classification",
	institution = "USC/Information Sciences Institute",
	year = "2006",
	number = "ISI-TR-2006-614",
	month = "April",
	keywords = "sensornet, traffic classification, sensor fusion",
	url = "http://www.isi.edu/~johnh/PAPERS/Park06a.html",
	pdfurl = "http://www.isi.edu/~johnh/PAPERS/Park06a.pdf",
	otherurl = "ftp://ftp.isi.edu/isi-pubs/tr-614.pdf",
}

Copyright

This paper is copyright © 2006 by its authors. Permission to make digital or hard copies of part or all of this work for personal use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that new copies bear this notice and the full citation on the first page. Abstracting with credit is permitted.

To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission of the authors.


© 2006 $Date$