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dc.contributor.authorGomes, Viviani Antunes-
dc.contributor.authorPitombo, Cira Souza-
dc.contributor.authorRocha, Samille Santos-
dc.contributor.authorSalgueiro, Ana Rita Gonçalves Neves Lopes-
dc.date.accessioned2022-03-09T19:00:04Z-
dc.date.available2022-03-09T19:00:04Z-
dc.date.issued2016-
dc.identifier.citationGOMES, Viviani Antunes et al. Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting. Open Journal of Statistics, [s.l.], v. 6, n. 3, p. 514-527, 2016.pt_BR
dc.identifier.issn2161-7198-
dc.identifier.urihttp://www.repositorio.ufc.br/handle/riufc/64354-
dc.description.abstractThis paper aims to compare the results of two techniques of Kriging (Ordinary Kriging and Indicator Kriging) that are applied to estimate the Private Motorized (PM) travel mode use (car or motorcycle) in several geographical coordinates of non-sampled values of the concerning variable. The data used was from the Origin/Destination and Public Transportation Opinion Survey, carried out in 2007/2008 at São Carlos (SP, Brazil). The techniques were applied in the region with 110 sample points (households). Initially, Decision Tree was applied to estimate the probability of mode choice in surveyed households, thus determining the numeric variable to be used in Ordinary Kriging. For application of Indicator Kriging it was used the variable “main travel mode” in a discrete manner, where “1” represented the use of PM travel mode and “0” characterized others travel modes. The results obtained by the two spatial estimation techniques were similar (Kriging maps and cross-validation procedure). However, the Indicator Kriging (KI) obtained the highest number of hit rates. In addition, with the KI it was possible to use the variable in its original form, avoiding error propagation. Finally, it was concluded that spatial statistics was thriving in travel demand forecasting issues, giving rise, for the both Kriging methods, to a travel mode choice surface on a confirmatory way.pt_BR
dc.language.isoenpt_BR
dc.publisherOpen Journal of Statisticspt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectGeostatisticspt_BR
dc.subjectKrigingpt_BR
dc.subjectTravel Mode Choicept_BR
dc.subjectSpatial Estimationpt_BR
dc.titleKriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecastingpt_BR
dc.typeArtigo de Periódicopt_BR
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