| Abstract
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<p>Trucks are designed, configured a … <p>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.</p><p>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.</p>s that warrant further analysis.</p>
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| Author
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Oskar Dahl +
, Fredrik Johansson +
, Reza Khoshkangini +
, Sepideh Pashami +
, Sławomir Nowaczyk +
, Claes Pihl +
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| Conference
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The 3rd International Conference on Information Management and Processing (ICIMP 2020), Portsmouth, United Kingdom, June 11-13, 2020
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| Diva
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http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1376856
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| PublicationType
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Conference Paper +
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| Title
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Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
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| Year
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2020 +
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| Has queryThis property is a special property in this wiki.
|
Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
, Publications:Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning +
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| Categories |
Publication +
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| Modification dateThis property is a special property in this wiki.
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17 February 2020 21:22:19 +
|
