International Journal of Science, Engineering and TechnologyAuthors: S.P.Gunjal, Kanifnath Gite, Rutuja Mokhar, Vishakha Nadgiri, Dhanshri Bachkar
Abstract: Traffic congestion remains one of the most pressing urban challenges globally, leading to increased travel time, fuel consumption, and pollution. This paper presents a software-only implementation of a Dynamic Traffic Light System (DTLS) powered by Edge Machine Learning (ML) techniques. Using an optimized YOLOv3-tiny model for real-time vehicle detection and intelligent traffic light timing algorithms, the system adapts dynamically to traffic conditions across multiple junctions. Simulation results on standard datasets demonstrate reduced vehicle wait times and enhanced emergency response capabilities, making the solution highly scalable for Intelligent Transportation Systems (ITS) and smart city integration.
