A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation
Autor(a) principal: | |
---|---|
Data de Publicação: | 2024 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/40965 |
Resumo: | Global positioning system data play a crucial role in comprehending an individual’s life due to its ability to provide geographic positions and timestamps. However, it is a challenge to identify the transportation mode used during a trajectory due to the large amount of spatiotemporal data generated, and the distinct spatial characteristics exhibited. This paper introduces a novel approach for transportation mode identification by transforming trajectory data features into image representations and employing these images to train a neural network based on vision transformers architectures. Existing approaches require predefined temporal intervals or trajectory sizes, limiting their adaptability to real-world scenarios characterized by several trajectory lengths and inconsistent data intervals. The proposed approach avoids segmenting or changing trajectories and directly extracts features from the data. By mapping the trajectory features into pixel location generated using a dimensionality reduction technique, images are created to train a deep learning model to predict five transport modes. Experimental results demonstrate a state-of-the-art accuracy of 92.96% on the Microsoft GeoLife dataset. Additionally, a comparative analysis was performed using a traditional machine learning approach and neural network architectures. The proposed method offers accurate and reliable transport mode identification applicable in real-world scenarios, facilitating the understanding of individual’s mobility. |
id |
RCAP_db9c8b22a26d9c986767f681b25ff1a3 |
---|---|
oai_identifier_str |
oai:ria.ua.pt:10773/40965 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representationGPS dataGPS trajectoriesDeepInsightVision transformersDeep learningTransportation modes identificationLifeloggingGlobal positioning system data play a crucial role in comprehending an individual’s life due to its ability to provide geographic positions and timestamps. However, it is a challenge to identify the transportation mode used during a trajectory due to the large amount of spatiotemporal data generated, and the distinct spatial characteristics exhibited. This paper introduces a novel approach for transportation mode identification by transforming trajectory data features into image representations and employing these images to train a neural network based on vision transformers architectures. Existing approaches require predefined temporal intervals or trajectory sizes, limiting their adaptability to real-world scenarios characterized by several trajectory lengths and inconsistent data intervals. The proposed approach avoids segmenting or changing trajectories and directly extracts features from the data. By mapping the trajectory features into pixel location generated using a dimensionality reduction technique, images are created to train a deep learning model to predict five transport modes. Experimental results demonstrate a state-of-the-art accuracy of 92.96% on the Microsoft GeoLife dataset. Additionally, a comparative analysis was performed using a traditional machine learning approach and neural network architectures. The proposed method offers accurate and reliable transport mode identification applicable in real-world scenarios, facilitating the understanding of individual’s mobility.Springer2024-03-06T15:16:44Z2024-02-10T00:00:00Z2024-02-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/40965eng2364-415X10.1007/s41060-024-00510-3Ribeiro, RicardoTrifan, AlinaNeves, António J. R.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T01:47:24Zoai:ria.ua.pt:10773/40965Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:20:09.002247Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation |
title |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation |
spellingShingle |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation Ribeiro, Ricardo GPS data GPS trajectories DeepInsight Vision transformers Deep learning Transportation modes identification Lifelogging |
title_short |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation |
title_full |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation |
title_fullStr |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation |
title_full_unstemmed |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation |
title_sort |
A deep learning approach for transportation mode identification using a transformation of GPS trajectory data features into an image representation |
author |
Ribeiro, Ricardo |
author_facet |
Ribeiro, Ricardo Trifan, Alina Neves, António J. R. |
author_role |
author |
author2 |
Trifan, Alina Neves, António J. R. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Ribeiro, Ricardo Trifan, Alina Neves, António J. R. |
dc.subject.por.fl_str_mv |
GPS data GPS trajectories DeepInsight Vision transformers Deep learning Transportation modes identification Lifelogging |
topic |
GPS data GPS trajectories DeepInsight Vision transformers Deep learning Transportation modes identification Lifelogging |
description |
Global positioning system data play a crucial role in comprehending an individual’s life due to its ability to provide geographic positions and timestamps. However, it is a challenge to identify the transportation mode used during a trajectory due to the large amount of spatiotemporal data generated, and the distinct spatial characteristics exhibited. This paper introduces a novel approach for transportation mode identification by transforming trajectory data features into image representations and employing these images to train a neural network based on vision transformers architectures. Existing approaches require predefined temporal intervals or trajectory sizes, limiting their adaptability to real-world scenarios characterized by several trajectory lengths and inconsistent data intervals. The proposed approach avoids segmenting or changing trajectories and directly extracts features from the data. By mapping the trajectory features into pixel location generated using a dimensionality reduction technique, images are created to train a deep learning model to predict five transport modes. Experimental results demonstrate a state-of-the-art accuracy of 92.96% on the Microsoft GeoLife dataset. Additionally, a comparative analysis was performed using a traditional machine learning approach and neural network architectures. The proposed method offers accurate and reliable transport mode identification applicable in real-world scenarios, facilitating the understanding of individual’s mobility. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-03-06T15:16:44Z 2024-02-10T00:00:00Z 2024-02-10 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/40965 |
url |
http://hdl.handle.net/10773/40965 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2364-415X 10.1007/s41060-024-00510-3 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799137843647348736 |