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Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-3272-4145
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2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Trucks are designed, configured and marketed for various working environments. There lies a concern whether trucks are used as intended by the manufacturer, as usage may impact the longevity, efficiency and productivity of the trucks.

In this paper we propose a framework that aims to extract costumers' vehicle behaviours from LVD in order to evaluate whether they align with vehicle configurations, so-called GTA parameters. GMMs are employed to cluster and classify various vehicle behaviors from the LVD. RBML was applied on the clusters to examine whether vehicle behaviors follow the GTA configuration. Particularly, we propose an approach based on studying associations that is able to extract insights on whether the trucks are used as intended. Experimental results shown that while for the vast majority of the trucks' behaviors seemingly follows their GTA configuration, there are also interesting outliers that warrant further analysis.

Place, publisher, year, edition, pages
2020.
Keywords [en]
Machine Learning, Clustering, Usage Behaviors, Association Rule Mining, Gaussian Mixture Models.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-41214OAI: oai:DiVA.org:hh-41214DiVA, id: diva2:1376856
Conference
The 3rd International Conference on Information Management and Processing (ICIMP 2020), Portsmouth, United Kingdom, June 11-13, 2020
Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2020-02-17

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Khoshkangini, RezaPashami, SepidehNowaczyk, Sławomir

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf