Quantitative Approach to Assess Social Equity in Road Networks

"Impact" refers to the distribution of benefits or costs within a population. In the context of road networks, the desired benefit is having high-quality infrastructure. Resources are allocated to meet this societal need, with road network condition serving as an indicator of infrastructure quality. This condition is often assessed using measures like the pavement condition index (PCI) or pavement serviceability rating (PSR). Such measures convey the physical state of road assets, with agencies like the New York State DOT using scales to rate pavement surface condition. To evaluate social equity, agencies can select road condition measures that suit their requirements and available data.

The Gini coefficient assesses inequality within societies, often used to compare income disparities. In road networks, it measures inequality in pavement condition scores. Visualized by the Lorenz curve, it shows deviations from perfect equality; a greater deviation signifies higher inequality. Calculated using a specific equation, the coefficient ranges from zero for perfect equality to higher values indicating increased inequality within the highway network.


Abstract

This study addresses the need to quantitatively assess social equity in road network performance, a challenge often approached qualitatively in existing literature. By proposing a quantitative approach and exploring numerical measures, the study aims to provide objective assessments. The approach is applied to street networks in Seattle and New York, integrating GIS-based visualization with socioeconomic demographics to gauge social equity levels. The proposed formulations and measures extend the use of quantitative metrics for decision-making support. Objective evaluations of social equity can aid in needs assessment and guide decision-making processes in infrastructure planning.

Team

Chirag Kothari, S.M.ASCE ; William J. O’Brien, Ph.D., P.E., M.ASCE; Junfeng Jiao; Nabeel Khwaja

Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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For more information, please visit: https://ascelibrary.org/doi/abs/10.1061/JITSE4.ISENG-2254