Spatio-temporal analysis of pedestrian activity in Loughborough using WiFi data

<p dir="ltr">This study aims to explore the spatio-temporal pedestrian dynamics in Loughborough town centre using WiFi data and machine learning. A Decision Tree algorithm was employed to predict pedestrian count, which were spatially interpolated to create detailed movement maps for 2024. The analysis identified peak activity in January, driven by post-holiday and term-start traffic, and a low in July. Market Place consistently showed the highest pedestrian volume, while Woodgate recorded the lowest, revealing notable spatial variations. These findings provide valuable insights for urban planning, supporting the optimization of public spaces and pedestrian infrastructure to enhance town centre vibrancy.</p>