Estimation of Travel Mode Choice Using Geostatistics: a Brazilian Case Study
Traditional methods for travel demand estimation are often built on socioeconomic and travel information. The information required to conduct such studies is costly and rarely available in developing countries. Besides, some conventional methods do not consider the spatial relationship of variables...
| Autores: | , , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2021 |
| País: | Brasil |
| Recursos: | Universidade Federal de Uberlândia (UFU) |
| Repositório: | Revista brasileira de cartografia - RBC (Online) |
| Idioma: | inglês |
| OAI Identifier: | oai:ojs.www.seer.ufu.br:article/54210 |
| Acesso em linha: | https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/54210 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Geoestatística Krigagem Indicadora Escolha Modal Ausência de Dados Geostatistics Indicator Kriging Travel Mode Choice Lack of Data |
| Resumo: | Traditional methods for travel demand estimation are often built on socioeconomic and travel information. The information required to conduct such studies is costly and rarely available in developing countries. Besides, some conventional methods do not consider the spatial relationship of variables and, in general, a large amount of socioeconomic and individual travel data is required. The key aim of this paper is to evaluate the importance of considering spatial information when estimating travel mode choices especially considering the lack of available data. The study area is the São Paulo Metropolitan Area (Brazil) and the dataset refers to an Origin-Destination Survey, conducted in 2007. This research paper analyzes the use of Geostatistics when estimating discrete travel mode choices. The results demonstrated a satisfactory outcome for the geostatistical approach. Finally, although socioeconomic and travel variables have greater explanatory power in predicting travel mode choices, spatial factors contribute to better understand the travel behavior and to provide further information when estimating spatially correlated data. |
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