Edge AI and 5G for Climate-Aware Smart Mobility: A Case Study of Urban Traffic and Environmental Sensing in Lagos

As urban environments face increasing pressures from climate variability and traffic congestion, the integration of 5G and edge AI technologies into smart mobility systems becomes essential. This study investigates how real-time environmental and traffic data can be used to optimize smart mobility in Lagos, Nigeria—a rapidly growing urban center. Using publicly available Lagos traffic APIs and environmental sensor data, we apply machine learning and RF propagation modeling to assess the deployment and performance of a climate-aware mobility system. The analysis demonstrates how environmental parameters such as temperature and humidity influence signal path loss, latency, and vehicle routing decisions. Our findings highlight the viability of edge-AI-enhanced systems for energy-efficient, resilient 5G communication in densely populated tropical urban settings.