Data-driven prioritising of pedestrian-oriented infrastructure: A framework for resources allocation

Achieving pedestrian-friendly cities, crucial for sustainable urban development, requires a deep understanding of pedestrian behaviour to guide infrastructure investment effectively. However, a significant gap exists in leveraging granular pedestrian data to inform these investments strategically, particularly to address historical inequities and enhance spatial justice in underserved areas. This research addresses this gap by investigating how integrated data analysis can identify and prioritize areas for pedestrian infrastructure investment, ensuring resource allocation maximises impact and promotes equity. Using Loughborough town centre as a case study, the study combines pedestrian activity data from Wi-Fi sensors with spatial data on existing infrastructure. A novel analytical framework is developed, classifying areas into four categories based on the alignment between pedestrian volume and infrastructural provision: Underutilised, Neglected, Effective Support, and Overburdened Zones. The findings demonstrate a significant mismatch between pedestrian movement patterns and existing infrastructure, pinpointing specific areas where high footfall is met with inadequate support. This nuanced classification proves superior to traditional planning methods by revealing strategic opportunities to address spatial inequities. Ultimately, this study provides a replicable, data-driven model for targeted resource allocation, enabling policymakers to make more informed and equitable decisions. By prioritising investment in underserved and overburdened areas, this approach fosters the creation of more inclusive, sustainable, and pedestrian-friendly urban environments.