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An adaptive learning-based approach for vehicle mobility prediction

2021-01-18Journal article Open access
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dc.contributor.authorIrio, Luís
dc.contributor.authorIp, Andre
dc.contributor.authorOliveira, Rodolfo
dc.contributor.authorLuís, Miguel
dc.date.accessioned2021-02-01T11:13:56Z
dc.date.available2021-02-01T11:13:56Z
dc.date.issued2021-01-18
dc.description.abstractThis 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationIRIO, Luís; [et al] – An adaptive learning-based approach for vehicle mobility prediction. IEEE Access. ISSN 2169-3536. Vol. 9 (2021), pp. 13671-13682pt_PT
dc.identifier.doi10.1109/ACCESS.2021.3052071pt_PT
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.21/12741
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9326302pt_PT
dc.subjectTrajectory predictionpt_PT
dc.subjectHidden Markov modelpt_PT
dc.subjectEstimation and modelingpt_PT
dc.subjectMachine learningpt_PT
dc.titleAn adaptive learning-based approach for vehicle mobility predictionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage13682pt_PT
oaire.citation.startPage13671pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume9pt_PT
person.familyNameIrio
person.familyNameIp
person.familyNameDuarte Oliveira
person.familyNameLuís
person.givenNameLuís
person.givenNameAndre
person.givenNameRodolfo Alexandre
person.givenNameMiguel
person.identifier.ciencia-idE91D-B30E-D631
person.identifier.ciencia-idA010-667D-7E1C
person.identifier.ciencia-id0815-1C89-5BD6
person.identifier.ciencia-id3418-A2F5-3CA4
person.identifier.orcid0000-0001-8081-0393
person.identifier.orcid0000-0003-1349-1315
person.identifier.orcid0000-0001-9181-8438
person.identifier.orcid0000-0003-3488-2462
person.identifier.scopus-author-id36164286400
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication94cdbbda-d866-4751-ac2c-e826adada8ed
relation.isAuthorOfPublicationafa47bc8-8d3d-4bc5-b006-f4a0ac28e028
relation.isAuthorOfPublication806977d3-2082-4636-9152-31dab560e777
relation.isAuthorOfPublication2eadcc1c-ff03-403a-9c42-f2e45f0fd528
relation.isAuthorOfPublication.latestForDiscovery2eadcc1c-ff03-403a-9c42-f2e45f0fd528

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