Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
Texto Completo: | http://www.repositorio.ufc.br/handle/riufc/64354 |
Resumo: | This 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. |
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Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecastingGeostatisticsKrigingTravel Mode ChoiceSpatial EstimationThis 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.Open Journal of Statistics2022-03-09T19:00:04Z2022-03-09T19:00:04Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfGOMES, 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, 2016.2161-7198http://www.repositorio.ufc.br/handle/riufc/64354Gomes, Viviani AntunesPitombo, Cira SouzaRocha, Samille SantosSalgueiro, Ana Rita Gonçalves Neves Lopesinfo:eu-repo/semantics/openAccessengreponame:Repositório Institucional da Universidade Federal do Ceará (UFC)instname:Universidade Federal do Ceará (UFC)instacron:UFC2023-10-10T17:23:37Zoai:repositorio.ufc.br:riufc/64354Repositório InstitucionalPUBhttp://www.repositorio.ufc.br/ri-oai/requestbu@ufc.br || repositorio@ufc.bropendoar:2024-09-11T18:16:03.051215Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting |
title |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting |
spellingShingle |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting Gomes, Viviani Antunes Geostatistics Kriging Travel Mode Choice Spatial Estimation |
title_short |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting |
title_full |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting |
title_fullStr |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting |
title_full_unstemmed |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting |
title_sort |
Kriging geostatistical methods for travel mode choice: a spatial data analysis to travel demand forecasting |
author |
Gomes, Viviani Antunes |
author_facet |
Gomes, Viviani Antunes Pitombo, Cira Souza Rocha, Samille Santos Salgueiro, Ana Rita Gonçalves Neves Lopes |
author_role |
author |
author2 |
Pitombo, Cira Souza Rocha, Samille Santos Salgueiro, Ana Rita Gonçalves Neves Lopes |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Gomes, Viviani Antunes Pitombo, Cira Souza Rocha, Samille Santos Salgueiro, Ana Rita Gonçalves Neves Lopes |
dc.subject.por.fl_str_mv |
Geostatistics Kriging Travel Mode Choice Spatial Estimation |
topic |
Geostatistics Kriging Travel Mode Choice Spatial Estimation |
description |
This 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. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2022-03-09T19:00:04Z 2022-03-09T19:00:04Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
GOMES, 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. 2161-7198 http://www.repositorio.ufc.br/handle/riufc/64354 |
identifier_str_mv |
GOMES, 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. 2161-7198 |
url |
http://www.repositorio.ufc.br/handle/riufc/64354 |
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.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Open Journal of Statistics |
publisher.none.fl_str_mv |
Open Journal of Statistics |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da Universidade Federal do Ceará (UFC) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Repositório Institucional da Universidade Federal do Ceará (UFC) |
collection |
Repositório Institucional da Universidade Federal do Ceará (UFC) |
repository.name.fl_str_mv |
Repositório Institucional da Universidade Federal do Ceará (UFC) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
bu@ufc.br || repositorio@ufc.br |
_version_ |
1813028730134593536 |