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Comparison of Machine Learning Techniques for Vehicle Classification using Road Side Sensors
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-6032-6155
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-5888-8626
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0069-640X
2015 (English)In: Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems: Las Palmas, 15-18 Sept. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 572-577, article id 7313192Conference paper, Published paper (Refereed)
Abstract [en]

The main contribution of this paper is a comparison of different machine learning algorithms for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard using measurements of road surface vibrations and magnetic field disturbances caused by vehicles. The algorithms considered are logistic regression, neural networks, and support vector machines. They are evaluated on a large dataset, consisting of 3074 samples and hence, a good estimate of the actual classification rate is obtained. The results show that for the considered classification problem logistic regression is the best choice with an overall classification rate of 93.4%.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015. p. 572-577, article id 7313192
National Category
Control Engineering Computer Sciences
Research subject
Control Engineering; Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-29521DOI: 10.1109/ITSC.2015.100ISI: 000376668800093Scopus ID: 2-s2.0-84950253616Local ID: 30720c89-e0b5-458f-a358-9c159fdc602cISBN: 978-1-4673-6595-6 (electronic)OAI: oai:DiVA.org:ltu-29521DiVA, id: diva2:1002745
Conference
International IEEE Conference on Intelligent Transportation Systems : 15/09/2015 - 18/09/2015
Note

Validerad; 2016; Nivå 1; 20150810 (wolfgang)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved

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Kleyko, DenisHostettler, RolandBirk, WolfgangOsipov, Evgeny

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