Intelligent traffic light systems using edge flow predictions

Adam Rizvi Thahir, Mustafa Coskun, Sultan Kübra Kiliç, Vehbi Cagri Gungor

Abstract: Highlights•With this work we aimed to optimize traffic signal systems by dynamically setting the system to behave as per predicted traffic flow.•Vehicle flow is predicted using Graph-based semi-supervised and active learning on edge flows.•The systems were created and tested using Simulation Of Urban Mobility (SUMO).•Vehicle data and routes were also generated using this application.•The proposed approach was compared against other methods, such as an occupancy based method, a scoring-based method and reinforcement learning.