Use of Walk Opportunities Index to Quantify Local Accessibility

Abstract

A research effort was undertaken by the Baltimore Metropolitan Council, Maryland, to explore further the relationship between land use and transportation to support regional planning analysis. Particular attention was given to quantifying the role of key land use factors such as density, diversity, and design (3Ds) and regional accessibility in determining rates of vehicle ownership and vehicle miles traveled (VMT). The research was facilitated by a recent regional household travel survey and significant untapped geographic information system (GIS) resources. What began as an effort simply to replicate the approach of an earlier published study that incorporated the 3Ds took an unexpected turn. The result was an entirely new measure, a walk opportunities index, which combines information on the number, character, and desirability of key activities located within walking distance of a household. GIS procedures were used to trace walk paths to each destination by using the street grid and to compute travel times while adjusting for topography and difficult street crossings. By using the gravity model paradigm, each opportunity was then discounted by its travel time and the results summed into a single measure of walk opportunities. When used in combination with measures of entropy, regional accessibility, and socioeconomic characteristics, this new index had important statistical value in regression models of household auto ownership and VMT. The ability of these models to address the travel effects of alternative transportation and land use development plans or policies makes them a potentially powerful tool in regional planning activities.

The Pleasure of Walking: An Innovative Methodology to Assess Appropriate Walkable Performance in Urban Areas to Support Transport Planning

“…In this way, it is possible not only for the easy estimation of PCAs, but the development of more complex composite indices and functions accordingly (for examples, the "Walkability Index-WI", and the "Walk Opportunities Index", as in Frank et al, Leslie et al and Kuzmyak et al. [51][52][53], or the walking cost distance function associated with links as in Corazza and Favaretto [38]). In this second type of measurements, along with GIS, the use of IT systems, such as the Personal Digital Assistant (PDA), which supports researchers in detecting certain specific characteristics of the built environment (mostly those related to the building stock, land use, vegetation, infrastructure), and can be used to assess walkability with more accuracy.…”

Spatial transferability assessment of a composite walkability index: The Pedestrian Index of the Environment (PIE)

“…• The Walkability Index (WI), most popular in the literature (Saelens et al, 2003) • The Walk Score®, a commercial solution (WS) (Koschinsky et al, 2016) • The Walk Opportunity Index (WOI) (Kuzmyak et al, 2006) • The Walkability Scale (WS) (Freeman et al, 2012) • The Pedshed (Ps) (Porta and Renne, 2005) • The Extended Walkability Index (EWI) and Moveability Index (MI) (Buck et al, 2014) • The Neighborhood Destination Accessibility Index (NDAI) (Witten et al, 2011) • The Pedestrian Index of the Environment (PIE), the focus of this paper (Singleton et al, 2014) Land use diversity X X X 3…”

Modelling walking accessibility: A case study of Ljubljana, Slovenia

“…In the previous calculations of walkability scores, distance most often has been taken into consideration by using fixed distance thresholds, also called a pedestrian shed ratio (ped shed) or walkable catchment areas/buffers, mostly based on the concept of a reasonable walking distance Kuzmyak et al, 2006;Lwin and Murayama, 2011;Porta and Renne, 2005;Witten et al, 2003). This method is very simple to use, but it has a number of drawbacks.…”

“…Although walkability indices in general are found to be a reliable and valid measure of estimating access to walkable amenities (Carr et al, 2010;Duncan et al, 2011), and have also performed quite well in describing pedestrian behaviours (Manaugh and El-Geneidy, 2011;Stockton et al, 2016;Weinberger and Sweet, 2012) or vehicle miles travelled (Kuzmyak et al, 2006), they have conceptual and computational limitations, as Vale et al (2015) argued in their extensive review of operational measures of active accessibility. For example, they can be less accurate in certain areas (Koschinsky et al, 2017), partly because they can mask within-buffer variations (Gutiérrez et al, 2011), or if they use Euclidean distance instead of the street network (Kozina, 2010).…”