How do socio-demographics and built environment affect individual accessibility based on activity space? Evidence from Greater Cleveland, Ohio

Abstract

Since the early 2000s, accessibility-based planning has been increasingly used to mitigate urban problems (e.g., traffic congestion and spatial mismatch) from a sustainable perspective. In particular, the concept of accessibility has been applied to investigate transport exclusion in many studies. However, few of them shed light on the effects of socio-demographics (e.g., income and gender) and the built environment (e.g., density) on accessibility at the individual level as a measure of transport exclusion. This study measures individual accessibility as the opportunities available per square mile within individual daily activity space for evaluating transport exclusion status based on the Capability Approach. Using data from the 2012 Northeast Ohio Regional Travel Survey and two opportunity sets (land uses and jobs), we calculate individual accessibility and compare them across three income groups. The comparisons report that low-income people are not disadvantaged in our study region. Path models are estimated to examine the relationships between socio-demographics, built environment, trip characteristics, and individual accessibility. We apply K-means cluster analysis to construct seven neighborhood types for the built environment. The results indicate that the effect of income on accessibility varies by opportunity types and living in urbanized neighborhoods increases people’s accessibility after controlling for other characteristics.

Does Adoption of Ridehailing Result in More Frequent Sustainable Mobility Choices? An Investigation Based on the National Household Travel Survey (NHTS) 2017 Data

“…For example, Bhat and Pulugurta used descriptors of residential location based on urbanization (Urban and Suburban) as proxy variables for other location-relevant factors, such as opportunities to perform activities by transit, level of service from auto and transit modes, and auto maintenance and insurance costs [52]. Also, Na Chen argued that sociodemographic variables can partially control for the effects of attitudinal variables [53]. Consequently, based on the assumption that predictors used in the analyses could control for the effects of other possible confounders, subsequent analyses were conducted and the results are interpreted.…”

Context-Adapted Urban Planning for Rapid Transitioning of Personal Mobility towards Sustainability: A Systematic Literature Review

“…As mentioned before, social justice and equity are emerging concepts in social sustainability of cities [7,11,12]. They can be analysed through the concepts of accessibility [9,19], utilisation of justice theory [104], or frameworks such as the one described by Boisjoly and Yengoh [73].…”

“…Other issues, such as questions of social sustainability, are gaining wider attention in the planning discourse. Equity, equality, and justice are important in transitioning towards sustainable mobility [7][8][9][10][11][12]. The same applies to different categories of passengers and the adaptation of the planning process in relation to societal changes, particularly demographic transition, migration, and "floating population" (those who live in rural areas, but work in urban areas).…”

A social equity analysis of the U.S. public transportation system based on job accessibility

“…Karner & Niemeier (2013) suggest that the use of a combination of indicators in one index to capture more than a single SES variable does not always accurately reflect a group's travel behavior since composite groups may not congregate spatially; thus, these indicators may reflect some combination of the group's travel behavior combined with other residents of the areal unit used for analysis. For guidance on transit equity as a component of social exclusion see Bocarejo & Oviedo, 2012;Burchardt, Le Grand, & Piachaud, 1999;Chen, & Akar, 2016;Currie & Delbosc, 2010;Kenyon, Lyons, & Rafferty, 2002; van Wee & Geurs, 2011;Witter, 2010. Since metropolitan areas are increasingly investing in public transportation systems, it is critical to address both obstacles and opportunities that planning for equity is likely to face when intersecting with politics (Bullard & Johnson, 1997;Bullard, Johnson, & Torres, 2004;Grengs, 2002;Whitt, 2014). Transit investments can be evaluated from many perspectives, e.g., user costs and benefits (e.g., accessibility, fares), service quality (e.g., congestion), external impacts (e.g., pollution, noise), economic impacts (e.g., economic opportunities, employment), and regulation and enforcement (e.g., traffic regulation) (Litman, 2016).…”

“…However, other results have been mixed; for example, some studies found shorter commute times for Black workers as compared to Whites (Zax, 1990), while others found shorter commute times for Whites from predominantly minority areas (Taylor & Ong, 1995). Some studies report that low-income and racial minorities have higher transit job accessibility than medium and high-income groups and Whites (Chen & Akar, 2016;Grengs, 2012); similarly, dramatic variations in job accessibility exist depending on residential location (Blumenberg & Ong, 2001) and vehicle ownership (Grengs, 2012;Shen, 1998). Karner & Golub (2015) demonstrated the critical role of the data source (e.g., census data vs. ridership data) in determining the outcome of a service equity analysis.…”