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

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

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...

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Bibliographic Details
Main Author: Oldenkamp, Emiel
Format: Others
Language:English
Published: Mälardalens högskola, Akademin för utbildning, kultur och kommunikation 2020
Subjects:
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
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-50977
Description
Summary: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.

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