Optimising On-Street Parking in Södermalm : A GIS-Based Framework for Sustainable Space Reduction of Parking Spaces

Sammanfattning

Parking oversupply remains a persistent challenge in many cities, contributing to excessive car use, inefficient land allocation, and increased carbon emissions. As Stockholm strives toward sustainable mobility, understanding where and how parking can be reduced has become essential. While previous research has explored correlations between car use and socioeconomic or behavioural factors, few studies have examined the actual balance between parking supply and demand due to the lack of complete and spatially explicit data. This absence of empirical evidence limits data-driven policymaking and may result in politically driven rather than evidence-based parking reforms. To address this gap, this study develops a comprehensive spatial dataset of parking spaces in Södermalm and applies a Geographic Information Systems (GIS)-based Multi-Criteria Decision Analysis (MCDA) combined with network analysis to identify priority areas for on-street parking removal. Six key criteria were considered: parking supply-demand ratio, garage vacancy, public transport accessibility, youth ratio, population density, and street hierarchy. Each criterion was weighted by experts to identify neighbourhoods with strong potential for parking space reduction. The analysis revealed significant parking oversupply in nearly half of Södermalm’s neighbourhoods, and identified specific areas where on-street parking could feasibly be relocated into existing garages. Building on these findings, we suggest that future research should expand the range of criteria, refine the parking dataset, and explore the use of artificial intelligence for more efficient data collection and analysis. Ultimately, the dataset, analytical framework, and findings presented here provide valuable insights for policymakers, planners, and property owners involved in Stockholm’s ongoing parking reform.