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

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 descriptorsthis 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 ongoing 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.

The Impact of Built Environment Characteristics on Energy Consumption Using Geographically Weighted Regression in Mashhad, Iran

“…A large number of previous studies have found the potential role of the built environment, and particularly of compact growth, in travel behavior and stabilizing global climates (Cervero&Kockelman,1997;Crane, 2000;Ewing & Cervero 2001;Ewing & Cervero 2010;Cervero&Murakami ,2010;Jahanshahi & Jin ,2016). The influence of the built environment and socio-economic characteristics on the energy consumption is examined with data aggregated to 253 traffic regions of Mashhad and analyzed through the OLS and GWR model.…”

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

“…Maldonado-Hinarejos et al 2014;Liao et al 2015;Delbosc and Naznin 2019;Jahanshahi et al 2019). The conceptual developments made in a suite of our three recent papers: Jahanshahi et al (2015) establishes an integrated path diagram for addressing selfselection, spatial sorting, car ownership endogeneity and interactions among trip purposes; Jahanshahi and Jin (2015) incorporates latent class variables into SEM and thus better quantify the effects of different types of built-up areas; Jahanshahi and Jin (2016) embeds random intercept SEM to quantify more precisely the influences of self-selection and spatial sorting in expanded market segmentation and built-up area typology.…”

“…Recent years have also seen a growing body of literature that aims to identify more precisely the effects of land use and the built environment on travel demand through better controls for interdependencies between travellers' socioeconomic and demographic profiles, social and cultural attitudes and car ownership. (Handy et al 2005;Van Acker et al 2007;Mokhtarian and Cao 2008;Gao et al 2008;Bohte et al 2009;Cao et al 2009;Sun et al 2009;Robert and Murakami 2010;Silva et al 2012;Sun et al 2012;Zegras et al 2012;Jin 2015, 2016;Jahanshahi et al 2015;Ding et al 2018). Notably, Gao et al (2008) analyse the connections between job accessibility, workers per capita, income per capita and cars per capita with census tract data for Sacramento, CA by employing a Structural Equation Model (SEM) to capture endogeneity effects.…”

“…Our previous papers (Jahanshahi et al 2015;Jin 2015, 2016) have provided a systematic coverage of the SEM applications regarding temporal shifts in the interdependencies, which means that we will focus here on additional literature that are cogent to analyses that combine LCA and SEM.…”

Re-thinking UK transport emissions – getting to the 2050 targets

“…Barcelona and Atlanta have populations of about 5 million people, but Barcelona's dense nature and plentiful public transport allows its citizens to expend just a tenth of the carbon dioxide emissions on transport that sprawling Atlanta requires (NCE, 2014). Jahanshahi and Jin (2015) suggest that there are three types of population density across the UK when considering the passenger transport distribution. They say 20% of the population lives in dense areas with access to good public transport and so can take advantage of it, while 30% live in low-density rural areas where…”