Varying influences of the built environment on household travel in 15 diverse regions of the United States

Abstract

This study pools household travel and built environment data from 15 diverse US regions to produce travel models with more external validity than any to date. It uses a large number of consistently defined built environmental variables to predict five household travel outcomes – car trips, walk trips, bike trips, transit trips and vehicle miles travelled (VMT). It employs multilevel modelling to account for the dependence of households in the same region on shared regional characteristics and estimates ‘hurdle’ models to account for the excess number of zero values in the distributions of dependent variables such as household transit trips. It tests built environment variables for three different buffer widths around household locations to see which scale best explains travel behaviour. The resulting models are appropriate for post-processing outputs of conventional travel demand models, and for sketch planning applications in traffic impact analysis, climate action planning and health impact assessment.

More Active Living–Oriented County and Municipal Zoning Is Associated With Increased Adult Leisure Time Physical Activity—United States, 2011

“…At the same time, the odds of adult walking were greater in counties with a higher proportion of the population covered by zoning requirements for bike lanes, bike parking (street furniture), MU, and active and passive recreation. The MU finding was consistent with the literature which has found associations between mixed land uses and more walkable communities (Heath et al, 2006; Ewing et al, 2014b; Ewing et al, 2011; Sallis & Glanz, 2006; Handy et al, 2002; Davison & Lawson, 2006; Ewing et al, 2003; Saelens et al, 2003b; Saelens et al, 2003a; Ewing & Cervero, 2010; Sallis et al, 2015). Changing zoning classifications (such as mixed use) through passing a new ordinance is much easier than changing the actual use for a parcel (Anderson et al, 2013).…”

“…Community characteristics that facilitate active living and PA include mixed-use (MU) developments and traditional neighborhood design that provide street and sidewalk connectivity, transportation infrastructure, and proximity to parks/recreational areas/facilities (Saelens et al, 2003b; Saelens et al, 2003a; Ewing et al, 2003; Davison & Lawson, 2006; Handy et al, 2002; Sallis & Glanz, 2006; Heath et al, 2006; Sallis et al, 2015). And, compact neighborhoods with dense street connectivity and MU are associated with increased adult walking (Berrigan & Troiano, 2002; Saelens et al, 2003b; Ewing et al, 2003; Li et al, 2005; Ewing & Cervero, 2010; Ewing et al, 2014b). Compact development has also been shown to have a positive effect on reducing obesity and chronic disease trends (Ewing et al, 2014a).…”

Predictive spatial network analysis for high-resolution transport modeling, applied to cyclist flows, mode choice, and targeting investment

“…Despite numerous studies of cycling-mode choice (Ewing et al, 2014;Parkin, Wardman, & Page, 2007;Wardman et al, 2007;Winters, Brauer, Setton, & Teschke, 2013) and route choice (Broach, Dill, & Gliebe, 2012;Ehrgott, Wang, Raith, & van Houtte, 2012), none so far have been turned into a general purpose tool for modeling cycling in the manner of the four-step model. Such a model would have applications in estimating change to mode choice from proposed cycle infrastructurethe key economic justification for investment-as well as highlighting hotspots where new infrastructure would be useful in the first place, assisting with option selection, and illustrating how proposed infrastructure fits in to the wider network (Forsyth & Krizek, 2011;Krizek, Handy, & Forsyth, 2009).…”

Land Use Mix and Pedestrian Travel Behavior: Advancements in Conceptualization and Measurement

“…When measured at the residential location, this latent construct had a stronger direct and total effect on increasing home-based, household-level pedestrian travel than those socio-economic characteristics tested in the theoretical model. Findings from the SEM corroborate generalizations of transportation-land use literature stating that trip distance is largely a function of the built environment, while mode choice is a function of both sociodemographic and built environment characteristics (Ewing, et al, 2015).…”

“…Analyzing the built environment determinants of travel in 15 regions, Ewing et al (2015) found an increase in the entropy score of three land use types (residential, commercial, and public) within a quarter-mile of the traveler's residence positively predicted walk mode choice. Earlier, Zhang and Kukadia (2005) Seattle-based study associating the entropy score of residential, office, retail, and entertainment land uses with walk mode choice.…”