Chen, NaAkar, Gulsah2017-05-012017-05-012017https://hdl.handle.net/11299/187858Since 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.enAccessibilityBuilt environmentCluster analysisArticle10.5198/jtlu.2016.861