An Adaptive Learning-Based Approach for Vehicle Mobility Prediction

Detalhes bibliográficos
Autor(a) principal: Irio, Luis
Data de Publicação: 2021
Outros Autores: Ip, Andre, Oliveira, Rodolfo, Luis, Miguel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/123686
Resumo: POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095
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spelling An Adaptive Learning-Based Approach for Vehicle Mobility Predictionestimation and modelinghidden Markov modelmachine learningTrajectory predictionComputer Science(all)Materials Science(all)Engineering(all)POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095This work presents an innovative methodology to predict the future trajectories of vehicles when its current and previous locations are known. We propose an algorithm to adapt the vehicles trajectories' data based on consecutive GPS locations and to construct a statistical inference module that can be used online for mobility prediction. The inference module is based on a hidden Markov model (HMM), where each trajectory is modeled as a subset of consecutive locations. The prediction stage uses the statistical information inferred so far and is based on the Viterbi algorithm, which identifies the subset of consecutive locations (hidden information) with the maximum likelihood when a prior subset of locations are known (observations). By analyzing the disadvantages of using the Viterbi algorithm (TDVIT) when the number of hidden states increases, we propose an enhanced algorithm (OPTVIT), which decreases the prediction computation time. Offline analysis of vehicle mobility is conducted through the evaluation of a dataset containing real traces of 442 taxis running in the city of Porto, Portugal, during a full year. Experimental results obtained with the dataset show that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time of the prediction process is significantly improved when OPTVIT is adopted and approximately 90% of prediction performance can be achieved, showing the effectiveness of the proposed method for vehicle trajectory prediction.DEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNIrio, LuisIp, AndreOliveira, RodolfoLuis, Miguel2021-09-03T00:12:10Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article12application/pdfhttp://hdl.handle.net/10362/123686engPURE: 28576362https://doi.org/10.1109/ACCESS.2021.3052071info: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:RCAAP2024-03-11T05:05:01Zoai:run.unl.pt:10362/123686Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:45:08.752720Repositório 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 An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
title An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
spellingShingle An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
Irio, Luis
estimation and modeling
hidden Markov model
machine learning
Trajectory prediction
Computer Science(all)
Materials Science(all)
Engineering(all)
title_short An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
title_full An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
title_fullStr An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
title_full_unstemmed An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
title_sort An Adaptive Learning-Based Approach for Vehicle Mobility Prediction
author Irio, Luis
author_facet Irio, Luis
Ip, Andre
Oliveira, Rodolfo
Luis, Miguel
author_role author
author2 Ip, Andre
Oliveira, Rodolfo
Luis, Miguel
author2_role author
author
author
dc.contributor.none.fl_str_mv DEE - Departamento de Engenharia Electrotécnica e de Computadores
RUN
dc.contributor.author.fl_str_mv Irio, Luis
Ip, Andre
Oliveira, Rodolfo
Luis, Miguel
dc.subject.por.fl_str_mv estimation and modeling
hidden Markov model
machine learning
Trajectory prediction
Computer Science(all)
Materials Science(all)
Engineering(all)
topic estimation and modeling
hidden Markov model
machine learning
Trajectory prediction
Computer Science(all)
Materials Science(all)
Engineering(all)
description POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095
publishDate 2021
dc.date.none.fl_str_mv 2021-09-03T00:12:10Z
2021
2021-01-01T00:00:00Z
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/10362/123686
url http://hdl.handle.net/10362/123686
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 28576362
https://doi.org/10.1109/ACCESS.2021.3052071
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12
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
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