Machine Learning and IoT Integration in Kigali Traffic

This paper presents the development and implementation of Machine Learning and IoT Integration in Kigali designed to mitigate urban congestion in Kigali, Rwanda. The system integrates real-time traffic monitoring, adaptive signal control, and predictive analytics to optimize traffic flow across the city's major corridors. The research follows the Structured System Analysis and Design Method (SSADM) and employs machine learning algorithms for traffic pattern prediction. The paper details system architecture, sensor integration, real-time processing capabilities, and implementation results from pilot deployment across five major intersections in Kigali. The study concludes that the proposed Machine Learning and IoT Integration in Kigali reduces average travel time by 32% and decreases fuel consumption by 28%. Additionally, it discusses the potential impact of AI-driven solutions in optimizing urban mobility and reducing environmental impact.