TrajectMe: planning sightseeing tours with hotel selection from trajectory data
| Ano de defesa: | 2018 |
|---|---|
| Autor(a) principal: | |
| Orientador(a): | |
| Banca de defesa: | |
| 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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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 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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Universidade Federal do Ceará |
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Universidade Federal do Ceará |
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