Time-evolving O-D matrix estimation using high-speed GPS data streams

Detalhes bibliográficos
Autor(a) principal: Luís Moreira Matias
Data de Publicação: 2016
Outros Autores: João Gama, Michel Ferreira, João Mendes Moreira, Damas,L
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5315
http://dx.doi.org/10.1016/j.eswa.2015.08.048
Resumo: Portable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based Expert and Intelligent Systems. The availability of these traces on the human mobility patterns is growing explosively. To mine this data is a fascinating challenge which can produce a big impact on both travelers and transit agencies. This paper proposes a novel incremental framework to maintain statistics on the urban mobility dynamics over a time-evolving origin-destination (O-D) matrix. The main motivation behind such task is to be able to learn from the location-based samples which are continuously being produced, independently on their source, dimensionality or (high) communicational rate. By doing so, the authors aimed to obtain a generalist framework capable of summarizing relevant context-aware information which is able to follow, as close as possible, the stochastic dynamics on the human mobility behavior. Its potential impact ranges Expert Systems for decision support across multiple industries, from demand estimation for public transportation planning till travel time prediction for intelligent routing systems, among others. The proposed methodology settles on three steps: (i) Half-Space trees are used to divide the city area into dense subregions of equal mass. The uncovered regions form an O-D matrix which can be updated by transforming the trees'leaves into conditional nodes (and vice-versa). The (ii) Partioning Incremental Algorithm is then employed to discretize the target variable's historical values on each matrix cell. Finally, a (iii) dimensional hierarchy is defined to discretize the domains of the independent variables depending on the cell's samples. A Taxi Network running on a mid-sized city in Portugal was selected as a case study. The Travel Time Estimation (TTE) problem was regarded as a real-world application. Experiments using one million data samples were conducted to validate the methodology. The results obtained highlight the straightforward contribution of this method: it is capable of resisting to the drift while still approximating context-aware solutions through a multidimensional discretization of the feature space. It is a step ahead in estimating the real-time mobility dynamics, regardless of its application field.
id RCAP_4977899c7a15ee037a56dfdac7834270
oai_identifier_str oai:repositorio.inesctec.pt:123456789/5315
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Time-evolving O-D matrix estimation using high-speed GPS data streamsPortable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based Expert and Intelligent Systems. The availability of these traces on the human mobility patterns is growing explosively. To mine this data is a fascinating challenge which can produce a big impact on both travelers and transit agencies. This paper proposes a novel incremental framework to maintain statistics on the urban mobility dynamics over a time-evolving origin-destination (O-D) matrix. The main motivation behind such task is to be able to learn from the location-based samples which are continuously being produced, independently on their source, dimensionality or (high) communicational rate. By doing so, the authors aimed to obtain a generalist framework capable of summarizing relevant context-aware information which is able to follow, as close as possible, the stochastic dynamics on the human mobility behavior. Its potential impact ranges Expert Systems for decision support across multiple industries, from demand estimation for public transportation planning till travel time prediction for intelligent routing systems, among others. The proposed methodology settles on three steps: (i) Half-Space trees are used to divide the city area into dense subregions of equal mass. The uncovered regions form an O-D matrix which can be updated by transforming the trees'leaves into conditional nodes (and vice-versa). The (ii) Partioning Incremental Algorithm is then employed to discretize the target variable's historical values on each matrix cell. Finally, a (iii) dimensional hierarchy is defined to discretize the domains of the independent variables depending on the cell's samples. A Taxi Network running on a mid-sized city in Portugal was selected as a case study. The Travel Time Estimation (TTE) problem was regarded as a real-world application. Experiments using one million data samples were conducted to validate the methodology. The results obtained highlight the straightforward contribution of this method: it is capable of resisting to the drift while still approximating context-aware solutions through a multidimensional discretization of the feature space. It is a step ahead in estimating the real-time mobility dynamics, regardless of its application field.2018-01-03T10:35:38Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5315http://dx.doi.org/10.1016/j.eswa.2015.08.048engLuís Moreira MatiasJoão GamaMichel FerreiraJoão Mendes MoreiraDamas,Linfo: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:RCAAP2023-05-15T10:20:37Zoai:repositorio.inesctec.pt:123456789/5315Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:23.434713Repositório 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 Time-evolving O-D matrix estimation using high-speed GPS data streams
title Time-evolving O-D matrix estimation using high-speed GPS data streams
spellingShingle Time-evolving O-D matrix estimation using high-speed GPS data streams
Luís Moreira Matias
title_short Time-evolving O-D matrix estimation using high-speed GPS data streams
title_full Time-evolving O-D matrix estimation using high-speed GPS data streams
title_fullStr Time-evolving O-D matrix estimation using high-speed GPS data streams
title_full_unstemmed Time-evolving O-D matrix estimation using high-speed GPS data streams
title_sort Time-evolving O-D matrix estimation using high-speed GPS data streams
author Luís Moreira Matias
author_facet Luís Moreira Matias
João Gama
Michel Ferreira
João Mendes Moreira
Damas,L
author_role author
author2 João Gama
Michel Ferreira
João Mendes Moreira
Damas,L
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Luís Moreira Matias
João Gama
Michel Ferreira
João Mendes Moreira
Damas,L
description Portable digital devices equipped with GPS antennas are ubiquitous sources of continuous information for location-based Expert and Intelligent Systems. The availability of these traces on the human mobility patterns is growing explosively. To mine this data is a fascinating challenge which can produce a big impact on both travelers and transit agencies. This paper proposes a novel incremental framework to maintain statistics on the urban mobility dynamics over a time-evolving origin-destination (O-D) matrix. The main motivation behind such task is to be able to learn from the location-based samples which are continuously being produced, independently on their source, dimensionality or (high) communicational rate. By doing so, the authors aimed to obtain a generalist framework capable of summarizing relevant context-aware information which is able to follow, as close as possible, the stochastic dynamics on the human mobility behavior. Its potential impact ranges Expert Systems for decision support across multiple industries, from demand estimation for public transportation planning till travel time prediction for intelligent routing systems, among others. The proposed methodology settles on three steps: (i) Half-Space trees are used to divide the city area into dense subregions of equal mass. The uncovered regions form an O-D matrix which can be updated by transforming the trees'leaves into conditional nodes (and vice-versa). The (ii) Partioning Incremental Algorithm is then employed to discretize the target variable's historical values on each matrix cell. Finally, a (iii) dimensional hierarchy is defined to discretize the domains of the independent variables depending on the cell's samples. A Taxi Network running on a mid-sized city in Portugal was selected as a case study. The Travel Time Estimation (TTE) problem was regarded as a real-world application. Experiments using one million data samples were conducted to validate the methodology. The results obtained highlight the straightforward contribution of this method: it is capable of resisting to the drift while still approximating context-aware solutions through a multidimensional discretization of the feature space. It is a step ahead in estimating the real-time mobility dynamics, regardless of its application field.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2018-01-03T10:35:38Z
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://repositorio.inesctec.pt/handle/123456789/5315
http://dx.doi.org/10.1016/j.eswa.2015.08.048
url http://repositorio.inesctec.pt/handle/123456789/5315
http://dx.doi.org/10.1016/j.eswa.2015.08.048
dc.language.iso.fl_str_mv eng
language eng
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.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_ 1799131608315330560