Corridor Detection from Large GPS Trajectories Datasets

Given the widespread use of mobile devices that track their geographical location, it has become increasingly easy to acquire information related to users' trips in real time. This availability has triggered several studies based on user's position, such as the analysis of flows of people...

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Detalles Bibliográficos
Autores: Cavallaro, Claudia, Vitrià i Marca, Jordi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/174901
Acceso en línea:https://hdl.handle.net/2445/174901
Access Level:acceso abierto
Palabra clave:Serveis de geolocalització
Dades geoespacials
Location-based services
Geospatial data
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repository_id_str
spelling Corridor Detection from Large GPS Trajectories DatasetsCavallaro, ClaudiaVitrià i Marca, JordiServeis de geolocalitzacióDades geoespacialsLocation-based servicesGeospatial dataGiven the widespread use of mobile devices that track their geographical location, it has become increasingly easy to acquire information related to users' trips in real time. This availability has triggered several studies based on user's position, such as the analysis of flows of people in cities, and also new applications, such as route recommendation systems. Given a dataset of geographical trajectories in an urbanmetropolitan area,we propose a algorithmto detect corridors. Corridors can be defined as geographical paths, with a minimum length, that are commonly traversed by a minimum number of different users. We propose an efficient strategy based on the Apriori algorithm to extract frequent trajectory patterns from the geo-spatial dataset. By discretizing the data and adapting the roles of itemsets and baskets of this algorithm to our context, we find the longest corridors formed by cells shared by a minimum number of trajectories. After that, we refine the results obtained with a subsequent filtering step, by using a Radius Neighbors Graph. To illustrate the algorithm, the GeoLife dataset is analyzed by following the proposed method. Our approach is relevant for transportation analytics because it is the base to detect lacking lines in public transportation systems and also to recommend to private users which route to take when moving from one part of the city to another on the basis of behavior of the users who provided their logs.MDPI2021202120202021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion16 p.application/pdfapplication/pdfhttps://hdl.handle.net/2445/174901Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.3390/app10145003Applied Sciences, 2020, vol. 10, num. 14https://doi.org/10.3390/app10145003cc-by (c) Cavallaro, Claudia et al., 2020http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1749012026-05-29T05:05:01Z
dc.title.none.fl_str_mv Corridor Detection from Large GPS Trajectories Datasets
title Corridor Detection from Large GPS Trajectories Datasets
spellingShingle Corridor Detection from Large GPS Trajectories Datasets
Cavallaro, Claudia
Serveis de geolocalització
Dades geoespacials
Location-based services
Geospatial data
title_short Corridor Detection from Large GPS Trajectories Datasets
title_full Corridor Detection from Large GPS Trajectories Datasets
title_fullStr Corridor Detection from Large GPS Trajectories Datasets
title_full_unstemmed Corridor Detection from Large GPS Trajectories Datasets
title_sort Corridor Detection from Large GPS Trajectories Datasets
dc.creator.none.fl_str_mv Cavallaro, Claudia
Vitrià i Marca, Jordi
author Cavallaro, Claudia
author_facet Cavallaro, Claudia
Vitrià i Marca, Jordi
author_role author
author2 Vitrià i Marca, Jordi
author2_role author
dc.subject.none.fl_str_mv Serveis de geolocalització
Dades geoespacials
Location-based services
Geospatial data
topic Serveis de geolocalització
Dades geoespacials
Location-based services
Geospatial data
description Given the widespread use of mobile devices that track their geographical location, it has become increasingly easy to acquire information related to users' trips in real time. This availability has triggered several studies based on user's position, such as the analysis of flows of people in cities, and also new applications, such as route recommendation systems. Given a dataset of geographical trajectories in an urbanmetropolitan area,we propose a algorithmto detect corridors. Corridors can be defined as geographical paths, with a minimum length, that are commonly traversed by a minimum number of different users. We propose an efficient strategy based on the Apriori algorithm to extract frequent trajectory patterns from the geo-spatial dataset. By discretizing the data and adapting the roles of itemsets and baskets of this algorithm to our context, we find the longest corridors formed by cells shared by a minimum number of trajectories. After that, we refine the results obtained with a subsequent filtering step, by using a Radius Neighbors Graph. To illustrate the algorithm, the GeoLife dataset is analyzed by following the proposed method. Our approach is relevant for transportation analytics because it is the base to detect lacking lines in public transportation systems and also to recommend to private users which route to take when moving from one part of the city to another on the basis of behavior of the users who provided their logs.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/174901
url https://hdl.handle.net/2445/174901
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/app10145003
Applied Sciences, 2020, vol. 10, num. 14
https://doi.org/10.3390/app10145003
dc.rights.none.fl_str_mv cc-by (c) Cavallaro, Claudia et al., 2020
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Cavallaro, Claudia et al., 2020
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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