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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Using supervised learning methods to predict the stop duration of heavy vehicles.

Oldenkamp, Emiel January 2020 (has links)
In this thesis project, we attempt to predict the stop duration of heavy vehicles using data based on GPS positions collected in a previous project. All of the training and prediction is done in AWS SageMaker, and we explore possibilities with Linear Learner, K-Nearest Neighbors and XGBoost, all of which are explained in this paper. Although we were not able to construct a production-grade model within the time frame of the thesis, we were able to show that the potential for such a model does exist given more time, and propose some suggestions for the paths one can take to improve on the endpoint of this project.
Mathematics
Applied Mathematics
Machine Learning
Supervised Learning
Regression
Linear Learner
Linear Regression
K-Nearest neighbors
Extreme Gradient Boosting
XGBoost
AWS SageMaker
Scania
Data Science
Data Analysis
Other Mathematics
Annan matematik

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