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The built environment typologies in the UK and their influences on travel behaviour: new evidence through latent categorisation in structural equation modelling

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  • Kaveh Jahanshahi
  • Ying Jin

Abstract

This paper uses a new latent categorisation approach (LCA) in structural equation modelling (SEM) to gain fresh insights into the influence of the built environment characteristics upon travel behaviour. So far as we are aware, this is the first LCA-SEM application in this field. We use all the main descriptors of the built environment in the UK National Travel Survey data in the analysis whilst accounting for the high correlations among the descriptors -- this is achieved through defining a categorical rather than continuous latent variable for the built environment characteristics. This novel approach to defining a tangible typology of the built environment in the UK is capable of making the analytical results more cogent to formulating new, proactive land use planning and urban design measures as well as monitoring the outcomes of on-going planning and transport interventions. Since travel survey data are regularly collected across a large number of cities in the world, our approach helps to guide the design of future travel surveys for those cities in a way that enhances the analysis and monitoring of the impacts of planning and transport policies on travel choices.

Suggested Citation

  • Kaveh Jahanshahi & Ying Jin, 2016. "The built environment typologies in the UK and their influences on travel behaviour: new evidence through latent categorisation in structural equation modelling," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(1), pages 59-77, February.
    • Handle: RePEc:taf:transp:v:39:y:2016:i:1:p:59-77
    DOI: 10.1080/03081060.2015.1108083
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    Cited by:

    1. Sfyridis, Alexandros & Agnolucci, Paolo, 2020. "Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling," Journal of Transport Geography, Elsevier, vol. 83(C).
    2. Zhao, Pengjun & Wan, Jie, 2021. "Land use and travel burden of residents in urban fringe and rural areas: An evaluation of urban-rural integration initiatives in Beijing," Land Use Policy, Elsevier, vol. 103(C).
    3. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    4. Islam, Md Rabiul & Saphores, Jean-Daniel M., 2022. "An L.A. story: The impact of housing costs on commuting," Journal of Transport Geography, Elsevier, vol. 98(C).
    5. Lucas, Karen & Philips, Ian & Mulley, Corinne & Ma, Liang, 2018. "Is transport poverty socially or environmentally driven? Comparing the travel behaviours of two low-income populations living in central and peripheral locations in the same city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 622-634.
    6. Chen Cao & Feng Zhen & Xianjin Huang, 2022. "How Does Perceived Neighborhood Environment Affect Commuting Mode Choice and Commuting CO 2 Emissions? An Empirical Study of Nanjing, China," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    7. Jiang, Wen & Feng, Tao & Timmermans, Harry J.P., 2020. "Latent class path model of intention to move house," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).

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