A Generic Betweenness Centrality-Based Approach for Urban Street Network Reallocation to Mitigate Climate Change Impacts

Abstract Urban areas face escalating climate change impacts, including rising temperatures and extreme weather events, exacerbated by urban heat islands (UHIs) and limited green spaces. With over 4 billion urban residents globally, cities must adapt by reallocating urban land, particularly street networks, which occupy 20–30% of urban surfaces. This study proposes a generic method to address two variants of street network reallocation: (1) maximizing reallocated lanes while minimizing increased travel distances, and (2) relocating congested areas from city centers to peripheral zones. Leveraging edge betweenness centrality (EBC), we develop two algorithms: L-EBC (lowest EBC arcs for space optimization) and H-EBC (highest EBC arcs for traffic redistribution). Applied to approximately 50 real urban street networks extracted from Open Street Map, L-EBC achieves high space reallocation with minimal impact on All-Pairs Shortest Paths (APSP), while H-EBC shifts traffic congestion outward using metrics like Mcenter (centroid displacement) and Mspread (spatial dispersion). The work acknowledges city-specific constraints but provides a theoretical foundation for planners to initiate data-driven reallocation. Future directions include integrating temperature models, satellite data, and urban planner heuristics to refine targeted interventions.