Capturing the impact of individual characteristics on transport accessibility and equity analysis

TLDR

Transport accessibility depends on individual characteristics such as age, income and gender, yet most equity studies ignore this personal component. The paper investigates how incorporating individual characteristics into logsum‑based accessibility measures affects transport equity analysis. Using London Travel Demand Survey data, the authors compare two logsum measures—one with and one without individual characteristics—and evaluate spatial, social and economic equity outcomes. Results show that excluding individual characteristics yields unreliable social and economic equity findings, masks Gini‑index disparities, and obscures actionable inequity drivers, though it has limited effect on spatial equity when aggregated.

Abstract

Transport accessibility experienced by an individual depends on their needs and abilities, as represented by their individual characteristics, such as age, income and gender. Although important from an equity perspective, the individual component of accessibility is currently ignored in most transport equity studies. This paper evaluates the impact of including individual characteristics into logsum-based accessibility measures for transport equity analysis. Using data from the London Travel Demand Survey (LTDS) 2011–13, two alternate logsum measures of accessibility are specified – with and without individual characteristics. An empirical analysis of spatial, social and economic equity is conducted using both the measures, and the outcomes are compared. The results clearly demonstrate that ignoring individual characteristics in logsum measures of accessibility can lead to unreliable outcomes for social and economic equity analysis, but do not add significant value when aggregated across large geographical zones for spatial equity analysis. Overall, ignoring individual characteristics masks the disparity in distribution of accessibility, as measured by the Gini index. Although not straightforward, the difference between accessibility patterns using the two logsum measures also yields insights into the possible causes of inequity, which can provide actionable inputs to policy makers. The study highlights that personal needs and abilities are often responsible for accessibility variations among individuals and ignoring them can result in a misleading picture of equity, as demonstrated quantitatively in this paper.