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
id ndltd-UPSALLA1-oai-DiVA.org-mdh-50977
record_format oai_dc
spelling ndltd-UPSALLA1-oai-DiVA.org-mdh-509772020-10-02T06:18:56ZUsing supervised learning methods to predict the stop duration of heavy vehicles.engOldenkamp, EmielMälardalens högskola, Akademin för utbildning, kultur och kommunikation2020MathematicsApplied MathematicsMachine LearningSupervised LearningRegressionLinear LearnerLinear RegressionK-Nearest neighborsExtreme Gradient BoostingXGBoostAWS SageMakerScaniaData ScienceData AnalysisOther MathematicsAnnan matematikIn 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. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-50977application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic 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
spellingShingle 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
Oldenkamp, Emiel
Using supervised learning methods to predict the stop duration of heavy vehicles.
description 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.
author Oldenkamp, Emiel
author_facet Oldenkamp, Emiel
author_sort Oldenkamp, Emiel
title Using supervised learning methods to predict the stop duration of heavy vehicles.
title_short Using supervised learning methods to predict the stop duration of heavy vehicles.
title_full Using supervised learning methods to predict the stop duration of heavy vehicles.
title_fullStr Using supervised learning methods to predict the stop duration of heavy vehicles.
title_full_unstemmed Using supervised learning methods to predict the stop duration of heavy vehicles.
title_sort using supervised learning methods to predict the stop duration of heavy vehicles.
publisher Mälardalens högskola, Akademin för utbildning, kultur och kommunikation
publishDate 2020
url http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-50977
work_keys_str_mv AT oldenkampemiel usingsupervisedlearningmethodstopredictthestopdurationofheavyvehicles
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