Rapid urbanization has increased private vehicle usage, generating intense parking demand in congested cities like Shiraz, Iran. The spatial distribution of parking is thus critical to sustainable urban transport, as a misalignment with local demand leads to prolonged travel times, higher fuel consumption costs, and elevated pollution, thereby impeding sustainable transportation planning. In this study, we aim to develop a statistical framework to assess equity in parking distribution in an urban context and address two core questions: how parking supply correlates with local demand and what the equity implications of this distribution are. To achieve this, we employ spatial statistical methods (ANNI, Kernel Density, and Moran’s I) and correlation analysis to examine parking supply and demand across 56 districts of Shiraz. Our analysis reveals statistically significant yet weak correlations between parking capacity and demand, indicating supply-demand mismatches across city zones that result in extended search times, increased congestion, higher fuel consumption, and amplified environmental impacts, thereby perpetuating socio-economic inequities. Overall, the innovation of this article lies in integrating spatial statistical methods with supplementary analyses as a framework to evaluate parking distribution, bridging the gap between quantitative descriptive analysis and justice-based assessments in the context of parking planning in an Iranian city.
Maktabifard et al. (Mon,) studied this question.