TrajectMe: planning sightseeing tours with hotel selection from trajectory data

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Oliveira, Emanuel Eduardo da Silva
Orientador(a): Macêdo, José Antonio Fernandes de
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal do Ceará
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://www.repositorio.ufc.br/handle/riufc/72256
Resumo: In this work, we propose TRAJECTME, an algorithm that solves the orienteering problem with hotel selection in several cities, taking advantage of the tourists’ trajectories extracted from location-based services. This method is an extension of the state-of-the-art memetic-based algorithm proposed by Ali Divsalar in 2014. To this end, we collect data from services such as Foursquare and Flickr to reconstruct the trajectories of tourists. Next, we build a hotel graph model (HGM) using a set of trajectories and a set of hotels to infer typical sequences of hotels and point of interest (PoI). The HGM is applied in the initialization phase and in the genetic operations of the memetic algorithm to provide sequences of hotels, whereas the associated sequence of PoIs evolved by applying local search moves. We evaluate our proposal using a large and real dataset from three Italian cities using up to 1000 hotels. The results show that the proposed algorithm outperforms the state-of-the-art when using large real datasets. Our approach is better than the baseline algorithm by up to 208% concerning the solution score and proved to be more profitable toward PoI visiting time, being 54% better than state-of-the-art.
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spelling Oliveira, Emanuel Eduardo da SilvaBrilhante, Igo RamalhoMacêdo, José Antonio Fernandes de2023-05-12T16:52:28Z2023-05-12T16:52:28Z2018OLIVEIRA, Emanuel Eduardo da Silva. TrajectMe: planning sightseeing tours with hotel selection from trajectory data. 2018. 60 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal do Ceará, Fortaleza, 2018.http://www.repositorio.ufc.br/handle/riufc/72256In this work, we propose TRAJECTME, an algorithm that solves the orienteering problem with hotel selection in several cities, taking advantage of the tourists’ trajectories extracted from location-based services. This method is an extension of the state-of-the-art memetic-based algorithm proposed by Ali Divsalar in 2014. To this end, we collect data from services such as Foursquare and Flickr to reconstruct the trajectories of tourists. Next, we build a hotel graph model (HGM) using a set of trajectories and a set of hotels to infer typical sequences of hotels and point of interest (PoI). The HGM is applied in the initialization phase and in the genetic operations of the memetic algorithm to provide sequences of hotels, whereas the associated sequence of PoIs evolved by applying local search moves. We evaluate our proposal using a large and real dataset from three Italian cities using up to 1000 hotels. The results show that the proposed algorithm outperforms the state-of-the-art when using large real datasets. Our approach is better than the baseline algorithm by up to 208% concerning the solution score and proved to be more profitable toward PoI visiting time, being 54% better than state-of-the-art.Neste trabalho propomos o TRAJECTME, um algoritmo para resolver o problema de orientação com a seleção de hotéis (OPHS, Orienteering Problem with Hotel Selection) a partir das trajetórias de turistas extraídas de serviços baseados em localização. Este método é uma extensão do algoritmo memético proposto por Ali Divsalar em 2014, estado-da-arte do problema em questão, também escolhido como baseline para comparação frente a solução proposta. Coletamos dados de serviços como Foursquare e Flickr para reconstruir as trajetórias dos turistas. Em seguida, construímos um modelo de grafo de hotéis (HGM, Hotel Graph Model) usando um conjunto de trajetórias e um conjunto de hotéis para inferir sequências típicas de hotéis e pontos de interesse (PoI). O HGM é aplicado na fase de inicialização e nas operações genéticas do algoritmo memético para fornecer sequências de hotéis, enquanto a sequência de PoIs evolui pela aplicação de movimentos de busca local. Avaliamos nossa proposta usando datasets reais de três cidades italianas que possuem centenas de hotéis e PoIs. Os resultados mostram que o algoritmo proposto supera o estado-da-arte em até 208% no score. Nosso algoritmo também faz mais uso do budget disponível, sendo até 54% melhor do que o baseline nessa métrica.Universidade Federal do CearáSightseeing tours planningHotel selectionTrajectoriesGenetic algorithmTrajectMe: planning sightseeing tours with hotel selection from trajectory datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisengreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFCinfo:eu-repo/semantics/openAccessORIGINAL2018_dis_eesoliveira.pdf2018_dis_eesoliveira.pdfapplication/pdf3698306http://repositorio.ufc.br/bitstream/riufc/72256/3/2018_dis_eesoliveira.pdf1216c2b16d4aab0aca7341be2fdba189MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ufc.br/bitstream/riufc/72256/4/license.txt8a4605be74aa9ea9d79846c1fba20a33MD54riufc/722562023-05-12 13:52:28.94oai:repositorio.ufc.br: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Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2023-05-12T16:52:28Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false
dc.title.pt_BR.fl_str_mv TrajectMe: planning sightseeing tours with hotel selection from trajectory data
title TrajectMe: planning sightseeing tours with hotel selection from trajectory data
spellingShingle TrajectMe: planning sightseeing tours with hotel selection from trajectory data
Oliveira, Emanuel Eduardo da Silva
Sightseeing tours planning
Hotel selection
Trajectories
Genetic algorithm
title_short TrajectMe: planning sightseeing tours with hotel selection from trajectory data
title_full TrajectMe: planning sightseeing tours with hotel selection from trajectory data
title_fullStr TrajectMe: planning sightseeing tours with hotel selection from trajectory data
title_full_unstemmed TrajectMe: planning sightseeing tours with hotel selection from trajectory data
title_sort TrajectMe: planning sightseeing tours with hotel selection from trajectory data
author Oliveira, Emanuel Eduardo da Silva
author_facet Oliveira, Emanuel Eduardo da Silva
author_role author
dc.contributor.co-advisor.none.fl_str_mv Brilhante, Igo Ramalho
dc.contributor.author.fl_str_mv Oliveira, Emanuel Eduardo da Silva
dc.contributor.advisor1.fl_str_mv Macêdo, José Antonio Fernandes de
contributor_str_mv Macêdo, José Antonio Fernandes de
dc.subject.por.fl_str_mv Sightseeing tours planning
Hotel selection
Trajectories
Genetic algorithm
topic Sightseeing tours planning
Hotel selection
Trajectories
Genetic algorithm
description In this work, we propose TRAJECTME, an algorithm that solves the orienteering problem with hotel selection in several cities, taking advantage of the tourists’ trajectories extracted from location-based services. This method is an extension of the state-of-the-art memetic-based algorithm proposed by Ali Divsalar in 2014. To this end, we collect data from services such as Foursquare and Flickr to reconstruct the trajectories of tourists. Next, we build a hotel graph model (HGM) using a set of trajectories and a set of hotels to infer typical sequences of hotels and point of interest (PoI). The HGM is applied in the initialization phase and in the genetic operations of the memetic algorithm to provide sequences of hotels, whereas the associated sequence of PoIs evolved by applying local search moves. We evaluate our proposal using a large and real dataset from three Italian cities using up to 1000 hotels. The results show that the proposed algorithm outperforms the state-of-the-art when using large real datasets. Our approach is better than the baseline algorithm by up to 208% concerning the solution score and proved to be more profitable toward PoI visiting time, being 54% better than state-of-the-art.
publishDate 2018
dc.date.issued.fl_str_mv 2018
dc.date.accessioned.fl_str_mv 2023-05-12T16:52:28Z
dc.date.available.fl_str_mv 2023-05-12T16:52:28Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.citation.fl_str_mv OLIVEIRA, Emanuel Eduardo da Silva. TrajectMe: planning sightseeing tours with hotel selection from trajectory data. 2018. 60 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal do Ceará, Fortaleza, 2018.
dc.identifier.uri.fl_str_mv http://www.repositorio.ufc.br/handle/riufc/72256
identifier_str_mv OLIVEIRA, Emanuel Eduardo da Silva. TrajectMe: planning sightseeing tours with hotel selection from trajectory data. 2018. 60 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal do Ceará, Fortaleza, 2018.
url http://www.repositorio.ufc.br/handle/riufc/72256
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.publisher.none.fl_str_mv Universidade Federal do Ceará
publisher.none.fl_str_mv Universidade Federal do Ceará
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Ceará (UFC)
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