A two-step method for mode choice estimation with socioeconomic and spatial information

Pitombo, Cira Souza; Salgueiro, Ana Rita; Costa, Aline Schindler Gomes da; Isler, Cassiano Augusto

doi:10.1016/j.spasta.2014.12.002

Cited by 32 publications

(27 citation statements)

References 23 publications

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“…However, the use of geostatistics in problems concerning the transportation demand or travel behavior is extremely recent (Gi and Gao, 2010;Peer et al, 2013;Pitombo et al, 2010;Pitombo et al, 2015)). So, the focus of this paper is to present not so trivial techniques in the study of travel behavior, for estimation of mode choice, incorporating spatial factors.…”

Section: Geostatistics and Transportmentioning

confidence: 99%

Proposal of a Sequential Method for Spatial Interpolation of Mode Choice

2015

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Resumo:O objetivo do presente trabalho é propor um método sequencial para interpolação espacial da escolha modal em domicílios não amostrados, através de técnicas de Árvore de Decisão e Geoestatística. Inicialmente, foi aplicada a técnica de Árvore de Decisão, com a finalidade de estimar a probabilidade de escolha do modo em domicílios pesquisados, determinando assim a variável numérica a ser krigada posteriormente. Os dados utilizados foram da Pesquisa OrigemDestino e da Pesquisa de Opinião sobre avaliação do transporte público, realizadas em 2007/2008 em São Carlos (SP). A área selecionada para modelagem geoestatística pelo estudo foi uma pequena região da cidade com 110 pontos amostrados. A escolha modal foi estimada na área estudada, revelando uma tendência de aumento da probabilidade do uso do carro do centro para a periferia da região. O método proposto pode ser uma alternativa às abordagens tradicionais, tanto na modelagem não-espacial, especialmente para o caso de falta de dados de pesquisa de preferência declarada, como em modelagem espacial, permitindo estimar valores em várias coordenadas geográficas. Abstract:The main objective of this study is to propose a sequential method for spatial interpolation of mode choice for household locations where choices are unobserved based on Decision Tree analysis and Geostatistics. Initially, Decision Tree analysis was applied in order to estimate the probability of mode choice in surveyed households, thus determining the numeric variable to be estimated by Ordinary Kriging. The data used is from the Origin-Destination Survey and Urban Transportation Evaluation Survey, carried out in 2007/2008 in the city of São Carlos (São Paulo/Brazil). The study area selected for geoestatistical modeling is a small region of the city with 110 sampling points. The mode choice was estimated for the study area revealing a tendency of increasing the probability of car usage from the center to the periphery of region. The proposed method can be an alternative to traditional approaches in both non-spatial modeling, especially for the case of lack of data from stated preference survey, as in spatial modeling, allowing estimation in various geographic coordinates.

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Section: Geostatistics and Transportmentioning

confidence: 99%

Proposal of a Sequential Method for Spatial Interpolation of Mode Choice

2015

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“…It is understood that urban travel issues are associated with individual and household features, as well as the spatial location of each household, destination and the activities distribution in the urban environment (Páez et al, 2013). Therefore, the incorporation of spatially correlated variables and the spatial position into studies of travel demand become important to enhance the quality of the estimations (Bhat and Sener, 2009;Páez and Scott, 2005;Ben-Akiva et al, 2004;Pitombo et al, 2015;Lindner et al, 2016;Gomes et al, 2016;Rocha et al, 2016).…”

Section: Introduction and Brief Literature Reviewmentioning

confidence: 99%

Proposal of a Geostatistical Procedure for Transportation Planning Field

2017

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Abstract:The main objective of this study is to estimate variables related to transportation planning, in particular transit trip production, by proposing a geostatistical procedure. The procedure combines the semivariogram deconvolution and Kriging with External Drift (KED). The method consists of initially assuming a disaggregated systematic sample from aggregate data. Subsequently, KED was applied to estimate the primary variable, considering the population as a secondary input. This research assesses two types of information related to the city of Salvador (Bahia, Brazil): an origin-destination dataset based on a home-interview survey carried out in 1995 and the 2010 census data. Besides standing out for the application of Geostatistics in the field of transportation planning, this paper introduces the concepts of semivariogram deconvolution applied to aggregated travel data. Thus far these aspects have not been explored in the research area. In this way, this paper mainly presents three contributions: 1) estimating urban travel data in unsampled spatial locations; 2) obtaining the values of the variable of interest deriving out of other variables; and 3) introducing a simple semivariogram deconvolution procedure, considering that disaggregated data are not available to maintain the confidentiality of individual data.

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“…health studies, where kriging techniques have been used for instance to identify areas of contamination or risk of mortality (Goovaerts, 2004(Goovaerts, , 2005(Goovaerts, , 2006(Goovaerts, , 2008(Goovaerts, , 2009). In transportation studies, its implementation has been explored in studies on traffic engineering (Ciuffo, Punzo, & Quaglietta, 2011;Mazzella, Piras, & Pinna, 2011;Zou, Yue, Li, & Yeh, 2012;Zhang & Wang, 2013), vehicle emission gases (Pearce, Rathbun, Aguilar-Villalobos, & Naeher, 2009;Kassteele & Velders 2006;Kassteele & Stein, 2006), and, more recently, to travel demand forecasting problems (Pitombo, Salgueiro, Costa, & Isler, 2015;Lindner, Pitombo, Rocha, & Quintanilha, 2016;Gomes, Pitombo, Rocha, & Salgueiro, 2016;Lindner & Pitombo, 2018). Specifically in traffic data, geostatistical tools have been implemented to analyze the spatial structure of the data under explanatory purposes (Majumdar, Noland, & Ochieng, 2004;Mcmillan, Hanson, & Lapham, 2007;Lascala, Johnson, & Gruenewald, 2001) or toward confirmatory analysis (Manepalli & Bham, 2011;Matsumono & Flores, 2013;Gundogdu, 2014;Molla, Stone, & Lee 2014).…”

Section: Spatial Statistics Methods On Crash Predictionmentioning

confidence: 99%

Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

Gomes¹

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A two-step method for mode choice estimation with socioeconomic and spatial information

Cited by 32 publications

References 23 publications

Proposal of a Sequential Method for Spatial Interpolation of Mode Choice

Proposal of a Sequential Method for Spatial Interpolation of Mode Choice

Proposal of a Geostatistical Procedure for Transportation Planning Field

Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches

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