Neighbourhood characteristics and bicycle commuting in the Greater London area

As the need to encourage modal shift from motorised vehicle use to active modes becomes greater, it is important to understand the key factors influencing the decision of how to travel. This paper explores the association between bicycle commuting and a range of sociodemographic and built and natural environment characteristics across wards and boroughs in Greater London, UK, with an aim to identify the key factors which influence participation. The authors employed a Bayesian multilevel heteroskedastic model with heterogeneity in variance, which can address dependencies in the data and unobserved heterogeneity more fully. This allowed us to account for unobserved/unmeasured covariates such as collective attitudes and the existence of cycling cultures that may differ between Greater London boroughs. They found that the propensity for bicycle commuting increases with an increase in the employment rate, the populations of white British and mixed white and black Caribbean, the proportion of terraced houses, and cycle network density. Conversely, they found that the propensity for bicycle commuting decreases with an increase in the absence of academic qualifications, the area of non-domestic buildings, the population of Indians and Pakistanis, and the number of cars per household. Their analysis also revealed important between-borough variations in the effect of key explanatory variables; notably, the effects of the populations of Indians, Pakistanis, and mixed white and black Caribbean, and the number of cars per household on the uptake of cycling to work all vary across Greater London boroughs. Finally, by allowing for heterogeneity in variance, they found that rates of bicycle commuting are more dispersed in Inner London and as the number of cars per household increases. The authors' analysis highlights the importance of cycling infrastructure in promoting bicycle commuting.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01891280
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 28 2023 9:19AM