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Utilize este identificador para citar ou criar um link para este item: http://acervodigital.unesp.br/handle/11449/135629
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dc.contributor.authorLopes, Simone-
dc.contributor.authorBrondino, Nair Cristina Margarido-
dc.contributor.authorSilva, Antônio Nélson Rodrigues da-
dc.date.accessioned2016-03-02T13:03:36Z-
dc.date.accessioned2016-10-25T21:33:07Z-
dc.date.available2016-03-02T13:03:36Z-
dc.date.available2016-10-25T21:33:07Z-
dc.date.issued2014-
dc.identifierhttp://dx.doi.org/10.3390/ijgi3020565-
dc.identifier.citationISPRS International Journal of Geo-Information, v. 3, n. 2, p. 565-583, 2014.-
dc.identifier.issn2220-9964-
dc.identifier.urihttp://hdl.handle.net/11449/135629-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/135629-
dc.description.abstractConsidering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included). This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.en
dc.format.extent565-583-
dc.language.isoeng-
dc.sourceCurrículo Lattes-
dc.subjectTransport planningen
dc.subjectTransport demanden
dc.subjectSpatial dependenceen
dc.subjectSpatial regressionen
dc.titleGIS-based analytical tools for transport planning: spatial regression models for transportation demand forecasten
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade de São Paulo (USP)-
dc.description.affiliationUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Matemática, Faculdade de Ciências de Bauru, Bauru, Av. Eng. Luiz E. Carrijo Coube S/N, Vargem Limpa, CEP 17033-360, SP, Brasil-
dc.description.affiliationDepartment of Transportation Engineering, São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São-carlense 400, 13566-590 São Carlos, Brazil-
dc.description.affiliationUnespUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Matemática, Faculdade de Ciências de Bauru, Bauru, Av. Eng. Luiz E. Carrijo Coube S/N, Vargem Limpa, CEP 17033-360, SP, Brasil-
dc.identifier.doi10.3390/ijgi3020565-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofISPRS International Journal of Geo-Information-
dc.identifier.lattes5603234988255497-
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