Residential self-selection and travel mode use in a new inner-city development neighbourhood in Berlin

Jarass, Julia; Scheiner, Joachim

doi:10.1016/j.jtrangeo.2018.05.018

Cited by 20 publications

(7 citation statements)

References 32 publications

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“…In line with the discussion on causalities, we argue that there is not one "best" causal structure, but there could be multiple. The reasons is the heterogeneity between people and households-see Cao and Chatman (2016), Jarass and Scheiner (2018), and Kim and Mokhtarian (2018). The challenge then is to find out which causal structure applies to which people or households.…”

Section: Multiple Causal Structuresmentioning

confidence: 99%

Residential self-selection in the relationship between the built environment and travel behavior: A literature review and research agenda

Wee

Cao

2022

Advances in Transport Policy and Planning

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This chapter gives an overview of the current debates on residential self-selection and presents a related research agenda. Here, we define residential self-selection as "the tendency of people to choose residential locations based on their travel abilities, needs and preferences." Debates relate to theory/causalities (including the role of attitudes), research methods, empirical findings (including the magnitude of the importance of residential self-selection for the influence of the built environment on travel behavior and the dominance of OECD (Organization for Economic Co-operation and Development) countries), and the implications for planning. The main contribution is Advances in Transport Policy and Planning # 2020 Elsevier Inc.

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Section: Multiple Causal Structuresmentioning

confidence: 99%

Residential self-selection in the relationship between the built environment and travel behavior: A literature review and research agenda

Wee

Cao

2022

Advances in Transport Policy and Planning

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“…2 Overview of the procedure steps for generating enhanced mobility types (EMT) from the survey data: A description of intermodal and unimodal mode use and respective trip purposes; B -obtain modality styles (intermodal styles and unimodal styles) from two cluster analyses; Cobtain mobility types by combining the intermodal and unimodal styles (clusters); D -obtain enhanced mobility types (EMT) by adding sociodemographic characteristics and mobility resources than monthly = 0.5; never = 0). This procedure was adopted from Jarass and Scheiner [24] to incorporate realistic frequency differences between answers in the survey. Afterwards, the frequency was added together over all trip purposes for each intermodal mode combination and for each unimodal usage separately.…”

Section: Survey Datamentioning

confidence: 99%

Developing a user typology considering unimodal and intermodal mobility behavior: a cluster analysis approach using survey data

Oostendorp

Nieland

Gebhardt

2019

Eur. Transp. Res. Rev.

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This paper aims to develop a user typology which enables user-specific analyses in respect of mobility behavior. It addresses the challenge of integrating unimodal and intermodal travel behavior into a user typology to obtain an overview of intermodal users within the context of their overall mobility behavior. The user typology is based on two cluster analyses (agglomerative hierarchical clustering) which use quantitative survey data on unimodal and intermodal mobility behavior obtained for Berlin, Germany. One cluster analysis was performed for unimodal use and one for intermodal mode use to take into account the users' relatively low use of intermodal modes as well. The analyses resulted in 6 intermodal and 5 unimodal clusters based on users' mobility behavior. Since in each case every individual is assigned to one intermodal and one unimodal cluster, the resulting intermodal and unimodal clusters were then combined in order to represent the overall mobility behavior of each individual as mobility types. The mobility types are further characterized by information on socio-demographics and mobility resources obtained from the dataset. These enhanced mobility types (EMT) provide a clearer impression of the users' characteristics and needs. This user typology takes account of the wide range of mobility options available in cities today and the resulting diversity in people's mobility behavior. To enable us to address the needs of users who combine several modes of transport within one trip, the proposed procedure approaches the challenge of integrating intermodal behavior into user types. The results provide a user typology which combines intermodal and unimodal travel behavior with personal characteristics and enable researchers and practitioners to work on user-specific research questions and planning tasks.

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“…Therefore, Mokhtarian and Cao suggested that the longitudinal structural equation model may be the best method to reveal the complex relationship between the built environment, residential self-selection, and travel behavior [39,42]. However, most studies on residential self-selection were based on cross-section data [49]. Therefore, most scholars verified residential self-selection based on the reverse causal judgment [2,3,[43][44][45]50].…”

Section: Introductionmentioning

confidence: 99%

Impact of Residential Self-Selection on Low-Carbon Behavior: Evidence from Zhengzhou, China

Zhang

Qin

et al. 2019

Sustainability

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Current resident lifestyles pose a significant threat to urban sustainable development. Therefore, low-carbon behavior is receiving increasing attention from scholars and policy makers. Ascertaining residential self-selection is essential in order to study the relationship between the built environment and travel behavior. While several studies have explored the relationship between the urban form, socioeconomic factors, and travel behavior, only a few of them have studied the impact of self-selection on household energy consumption and other forms of consumption, which are also contribute to household carbon emissions. Using large-scale field surveys of 1,485 households and high-resolution images, sourced from Google Maps in 2018, of Zhengzhou city, the present study estimated the low-carbon level of three kinds of behavior: daily energy use at home, daily travel, and daily consumption. The study investigated the influence factors on low-carbon behavior using the hierarchical linear model. We found that residential self-selection impacts both energy use and daily travel. Residents in some built environments consumed less energy at home and contributed less CO2 emissions through daily travel than others. In particular, individual-level variables significantly affected the low-carbon energy use behavior. The female, elderly, highly educated, married, and working-class residents with children had higher levels of low-carbon energy use. Community-level variables significantly affected the level of low-carbon travel and low-carbon consumption. If residents lived in areas with high density, mixed land use, and high accessibility, their travel mode and consumption behavior would entail low carbon emissions. There is a relationship between individual variables and community variables. Different individual attributes living in the same built environment have different impacts on low-carbon behaviors.

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Residential self-selection and travel mode use in a new inner-city development neighbourhood in Berlin

Cited by 20 publications

References 32 publications

Residential self-selection in the relationship between the built environment and travel behavior: A literature review and research agenda

Residential self-selection in the relationship between the built environment and travel behavior: A literature review and research agenda

Developing a user typology considering unimodal and intermodal mobility behavior: a cluster analysis approach using survey data

Impact of Residential Self-Selection on Low-Carbon Behavior: Evidence from Zhengzhou, China

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