The ability to navigate through a continuously changing business landscape has been a success factor for Scania to stay a competitive business, when the landscape continues to change. Digitalization has enabled data to be collected from various sources and the ability to embrace the possibilities that come with it and turn it into an advantage is crucial to make sure that Scania is driving the changing industry. Today, Scania is good at collecting and analyzing data but there is room for improvements when it comes to utilizing the data to create data-driven decision-making. This study aims to investigate the possibility of learning more about the users driving behavior through data-driven driving evaluation. This is done with a machine learning approach where a CNN-GRU neural network with an XGBoost classifier is created to classify triaxial acceleration data into normal or aggressive driving behavior. The findings show that this model architecture has a classification accuracy of 87.80 % and the result is discussed with respect to method implementation, quality of data, hyperparameter tuning, and future studies.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-226079 |
| Date | January 2024 |
| Creators | Lundberg, Henrik |
| Publisher | Umeå universitet, Institutionen för matematik och matematisk statistik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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