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

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  • Jarass, Julia
  • Scheiner, Joachim

Abstract

The purpose of this study is to analyse the effects of residential and travel preferences on mode use in a new inner-city development in Berlin. In contrast to the majority of related studies, we consider these preferences in a more holistic way by grouping residents into clusters. The kind of inner-city neighbourhood we studied particularly attracts families and couples who are affluent and have high levels of car ownership. However, a comparison of residents' mode choice with that of the general population of inner and outer Berlin shows that this kind of inner-city neighbourhood encourages even those households who are known to prefer driving to use modes other than the car. Moreover, we found variety in mode use which could be traced to the variation in reasons for residential choice (as an outcome of residential and travel preferences). This underlines the fact that preferences have a strong effect on travel behaviour, even in a homogeneous neighbourhood. Overall, the study suggests that new inner-city development neighbourhoods with a variety of housing types provide opportunities for sustainable daily mobility. At the same time the study area could have been better adapted to the various preferences of its future residents – for example, by reducing the number of parking spaces to further stimulate use of sustainable modes of transport.

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  • Jarass, Julia & Scheiner, Joachim, 2018. "Residential self-selection and travel mode use in a new inner-city development neighbourhood in Berlin," Journal of Transport Geography, Elsevier, vol. 70(C), pages 68-77.
    • Handle: RePEc:eee:jotrge:v:70:y:2018:i:c:p:68-77
    DOI: 10.1016/j.jtrangeo.2018.05.018
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    2. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.
    3. Fengying Yan & Ningyu Huang & Yehui Zhang, 2022. "How Can the Layout of Public Service Facilities Be Optimized to Reduce Travel-Related Carbon Emissions? Evidence from Changxing County, China," Land, MDPI, vol. 11(8), pages 1-24, July.
    4. Suman K. Mitra & Jean-Daniel M. Saphores, 2020. "How do they get by without cars? An analysis of travel characteristics of carless households in California," Transportation, Springer, vol. 47(6), pages 2837-2858, December.
    5. Zhao, Juanjuan & Ren, Huan & Gu, Yan & Pan, Haojie, 2023. "Relationships between the residential environment, travel attitude and behaviour among knowledge workers: The role of job types," Journal of Transport Geography, Elsevier, vol. 106(C).
    6. Yu, Le & Xie, Binglei & Chan, Edwin H.W., 2019. "Exploring impacts of the built environment on transit travel: Distance, time and mode choice, for urban villages in Shenzhen, China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 57-71.
    7. Faan Chen & Adriano Borges Costa, 2024. "Exploring the causal effects of the built environment on travel behavior: a unique randomized experiment in Shanghai," Transportation, Springer, vol. 51(1), pages 215-245, February.

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