An exploratory analysis of relationships between socioeconomic, land use, activity participation variables and travel patterns

Pitombo, Cira Souza; Kawamoto, Elisa Mitiko; Sousa, Antonio Jorge Gonçalves de

doi:10.1016/j.tranpol.2010.10.010

Cited by 49 publications

(43 citation statements)

References 18 publications

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“…This view is also reflects a long-standing body of theory on the relationship between the built environment and the transport sector (Cervero and Kockelman, 1997;Ewing and Cervero, 2010). Specifically, Banister (2005) identified six groups of key factors that interconnect the built environment and transport: settlement size (Hickman and Banister, 2007;Naess, 2009); urban density (Oakes et al, 2007;Liu et al, 2014;Soria-Lara and Valenzuela-Montes, 2014); land use diversity (Pitombo et al, 2010;Song and Knaap, 2004;; urban design; local accessibility (Cervero et al, 2009); and finally the provision of parking (Albert and Mahalel, 2006). Supported by the abovementioned issues, there has been a proliferation of studies based on correlating transport CO 2 emissions and built environment factors as an initial step to designing transport policy packages for CO 2 mitigation (Bart, 2010).…”

Section: Introductionmentioning

confidence: 84%

The influence of lifestyle and built environment factors on transport CO2 emissions: the case study of Autonomous University of Barcelona

Soria-Lara

Miralles‐Guasch

Marquet

2017

ACE: Architecture, City and Environment

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Structured abstractTransport is a major user of carbon-based fuels and is seen as crucial intervention sector for meeting CO 2 emission reduction targets. While the academic literature has traditionally focused more on correlating built environment factors (i.e. urban density, trip distance, etc.) and production of CO 2 in the transport sector, only limited attention has been paid to the influence of lifestyle factors. This paper examines the effects of lifestyle and built environments factors on transport CO 2 emissions generated by the daily commutes to and from the Autonomous University of Barcelona (UAB) in Greater Barcelona (Spain). The analysis revealed that lifestyle choices were as relevant as the built environment for understanding the emitting sources and CO 2 volume. Accordingly, the study provides insights how the design of efficient transport policy packages can integrate lifestyle factors as a central focal point.

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Section: Introductionmentioning

confidence: 84%

The influence of lifestyle and built environment factors on transport CO2 emissions: the case study of Autonomous University of Barcelona

Soria-Lara

Miralles‐Guasch

Marquet

2017

ACE: Architecture, City and Environment

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“…Buys and Miller [25] demonstrated that utilitarian and psycho-social elements such as travel purpose and time-efficiency were strongly related to urban residents' travel mode choice. Parking charges [26], activity participation [27], and multiple identities [28] also had an effect on residents' travel mode. Paulssen et al [29] suggested that personal values and individual attitudes impact travel mode choices.…”

Section: Travel Mode Choicementioning

confidence: 98%

Green Travel: Exploring the Characteristics and Behavior Transformation of Urban Residents in China

2017

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Green travel can ease urban traffic problems and has aroused Chinese government interest regarding their extensive concerns. Although the Chinese government has formulated some policies to encourage residents to perform green travel behavior, non-green travel behavior is still popular. Using the Q method, this study explores the characteristics of Chinese urban residents from the perspective of green travel behavior. The results show that according to values and travel attitudes, urban residents can be divided into three groups; green, light green, and brown travelers. Green travelers care about the environment, have positive environmental attitudes, and implement green travel in their daily lives. Light green travelers require incentives and good conditions to carry out green travel. Brown travelers, however, hold egoistic values and their travel behavior, with little consideration of environment, is mainly affected by individual travel habits. Such results can help governments make suitable and effective policies and initiatives to promote behavior transformation from brown and light green into green travelers.

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“…The transportation issue explored is a comparison of travel demand patterns of men and women in Israel. Pitombo et al (2011) analyzed relationships between socioeconomic, land use, activity participation and travel patterns with Decision Tree (Classification and Regression Tree algorithm) modelling.…”

Section: Data Mining and Travel Mode Choicesmentioning

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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An exploratory analysis of relationships between socioeconomic, land use, activity participation variables and travel patterns

Cited by 49 publications

References 18 publications

The influence of lifestyle and built environment factors on transport CO2 emissions: the case study of Autonomous University of Barcelona

The influence of lifestyle and built environment factors on transport CO2 emissions: the case study of Autonomous University of Barcelona

Green Travel: Exploring the Characteristics and Behavior Transformation of Urban Residents in China

Proposal of a Sequential Method for Spatial Interpolation of Mode Choice

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