Urban Mobility Data Analysis for Predictive Modeling: Exploiting Existing IoT Infrastructure

Abstract

This work presents a comprehensive analysis of urban mobility data collected by a pre-existing IoT infrastructure of 12 sensors in Villanueva de la Serena (Badajoz, 26,000 inhabitants). Through the development of an analytical dashboard in Streamlit, 12M records (January-December 2025) of pedestrian and vehicular flows are processed and visualized. The multidimensional analysis reveals significant temporal and spatial patterns, demonstrating the potential of these data to (1) support evidence-based urban planning decisions, and (2) serve as a foundation for future machine learning models capable of predicting mobility behaviors in similar scenarios. The results validate the strategic value of investments in urban sensor infrastructure.