Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters -- Using Clustering and Rule-Based Machine Learning

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Title Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters -- Using Clustering and Rule-Based Machine Learning
Author Oskar Dahl and Fredrik Johansson and Reza Khoshkangini and Sepideh Pashami and Sławomir Nowaczyk and Claes Pihl
Year 2020
PublicationType Conference Paper
Journal
HostPublication
Conference The 3rd International Conference on Information Management and Processing
DOI
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1376856
Abstract 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.