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Identification and mapping of spatial variations in travel choices through combining structural equation modelling and latent class analysis: findings for Great Britain

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  • Kaveh Jahanshahi

    (University of Cambridge)

  • Ying Jin

    (University of Cambridge)

Abstract

This paper exploits some latest advances in structural equation modelling and latent class analysis for identification and mapping of the spatial variations in travel choices. The approach controls for a wide range of socioeconomic and demographic variables and changes in car fuel prices. The research is focused on employed and self-employed adults, and the method can be readily extended to cover other travellers where such needs arise. The developed methodology enables us to overcome some of the persistent issues that have in the past prevented researchers making a full use of highly correlated and endogenous variables found in good-quality, comprehensive travel surveys at the national or metropolitan scales. Empirical findings from an application of the methodology for Great Britain provide a precise geographical classification of neighbourhoods areas across Britain and reveal the extent to which land use and built form influence commuting travel choices, whilst accounting for residents’ self-selection, spatial sorting and endogenous interactions among the explanatory variables. The results are cogent for defining spatially adapted strategies for planning new transport and land use interventions, particularly in areas that are expected to grow the most in the coming decades.

Suggested Citation

  • Kaveh Jahanshahi & Ying Jin, 2021. "Identification and mapping of spatial variations in travel choices through combining structural equation modelling and latent class analysis: findings for Great Britain," Transportation, Springer, vol. 48(3), pages 1329-1359, June.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:3:d:10.1007_s11116-020-10098-9
    DOI: 10.1007/s11116-020-10098-9
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