Understanding usage of Volvo trucks

• Main Authors: Dahl, Oskar, Johansson, Fredrik
• Format: Others
• Language: English
• Published: Högskolan i Halmstad, Akademin för informationsteknologi 2019
• Subjects: Machine Learning; Clustering; Usage Behaviors; Association Rule Mining; Gaussian Mixture Models; Robotics; Robotteknik och automation
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-40826

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 thesis we propose a framework divided into two separate parts, that aims to extract costumers’ driving behaviours from Logged Vehicle Data (LVD) in order to a): evaluate whether they align with so-called Global Transport Application (GTA) parameters and b): evaluate the usage in terms of performance. Gaussian mixture model (GMM) is employed to cluster and classify various driving behaviors. Association rule mining was applied on the categorized clusters to validate that the usage follow GTA configuration. Furthermore, Correlation Coefficient (CC) was used to find linear relationships between usage and performance in terms of Fuel Consumption (FC). It is found that the vast majority of the trucks seemingly follow GTA parameters, thus used as marketed. Likewise, the fuel economy was found to be linearly dependent with drivers’ various performances. The LVD lacks detail, such as Global Positioning System (GPS) information, needed to capture the usage in such a way that more definitive conclusions can be drawn. === <p>This thesis was later conducted as a scientific paper and was submit- ted to the conference of ICIMP, 2020. The publication was accepted the 23th of September (2019), and will be presented in January, 2020.</p>