Spatio-temporal modelling and mapping of network coverage for connected vehicles
Digital connectivity plays a major role in enabling sustainable transport solutions. A key ingredient that ensures this connectivity is the cellular network coverage, whose information is typically available from the service providers. However, from a practical point of view, the actual connectivity...
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Uppsala universitet, Institutionen för informationsteknologi
2022
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ftuppsalauniv:oai:DiVA.org:uu-483651 2023-05-15T17:44:34+02:00 Spatio-temporal modelling and mapping of network coverage for connected vehicles Pathare, Deepthi 2022 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-483651 eng eng Uppsala universitet, Institutionen för informationsteknologi IT 22 066 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-483651 info:eu-repo/semantics/openAccess Engineering and Technology Teknik och teknologier Student thesis info:eu-repo/semantics/bachelorThesis text 2022 ftuppsalauniv 2023-02-23T22:00:09Z Digital connectivity plays a major role in enabling sustainable transport solutions. A key ingredient that ensures this connectivity is the cellular network coverage, whose information is typically available from the service providers. However, from a practical point of view, the actual connectivity experienced by the vehicles may vary significantly. In this study, we implement machine learning methods to build a network connectivity map for connected vehicles using minimal ingredients such as data from Scania vehicles, cellular tower locations and geographical features. We compare the performance of four machine learning models -Random Forest, XGBoost, AdaBoost and Neural Networks, to predict the delay time in receiving the messages from vehicles. Our results show that Random Forest is the best model for predicting connectivity with a Root Median Squared Error of 1.38sec. We demonstrate the results by building a connectivity map for Northern Sweden. Bachelor Thesis Northern Sweden Uppsala University: Publications (DiVA) |
institution |
Open Polar |
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Uppsala University: Publications (DiVA) |
op_collection_id |
ftuppsalauniv |
language |
English |
topic |
Engineering and Technology Teknik och teknologier |
spellingShingle |
Engineering and Technology Teknik och teknologier Pathare, Deepthi Spatio-temporal modelling and mapping of network coverage for connected vehicles |
topic_facet |
Engineering and Technology Teknik och teknologier |
description |
Digital connectivity plays a major role in enabling sustainable transport solutions. A key ingredient that ensures this connectivity is the cellular network coverage, whose information is typically available from the service providers. However, from a practical point of view, the actual connectivity experienced by the vehicles may vary significantly. In this study, we implement machine learning methods to build a network connectivity map for connected vehicles using minimal ingredients such as data from Scania vehicles, cellular tower locations and geographical features. We compare the performance of four machine learning models -Random Forest, XGBoost, AdaBoost and Neural Networks, to predict the delay time in receiving the messages from vehicles. Our results show that Random Forest is the best model for predicting connectivity with a Root Median Squared Error of 1.38sec. We demonstrate the results by building a connectivity map for Northern Sweden. |
format |
Bachelor Thesis |
author |
Pathare, Deepthi |
author_facet |
Pathare, Deepthi |
author_sort |
Pathare, Deepthi |
title |
Spatio-temporal modelling and mapping of network coverage for connected vehicles |
title_short |
Spatio-temporal modelling and mapping of network coverage for connected vehicles |
title_full |
Spatio-temporal modelling and mapping of network coverage for connected vehicles |
title_fullStr |
Spatio-temporal modelling and mapping of network coverage for connected vehicles |
title_full_unstemmed |
Spatio-temporal modelling and mapping of network coverage for connected vehicles |
title_sort |
spatio-temporal modelling and mapping of network coverage for connected vehicles |
publisher |
Uppsala universitet, Institutionen för informationsteknologi |
publishDate |
2022 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-483651 |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_relation |
IT 22 066 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-483651 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1766146806249422848 |