Optimizing Urban Traffic Management Through AI with Digital Twin Simulation and Validation

Abstract

The number of vehicles on the road continuously increases, revealing a lack of robust and effective traffic management systems in urban settings. Urban traffic makes up a substantial portion of the total traffic problem, and current traffic light architecture has been limiting the traffic flow noticeably. This thesis focuses on developing an artificial intelligence-based smart traffic management system using a double duelling deep Q network (DDDQN), validated through a user-controlled 3D simulation, determining the system’s effectiveness. This work leverages current fisheye camera architecture to present a system that can be implemented into current architecture with little intrusion. The challenges surrounding large computer vision datasets, and the challenges and limitations surrounding fisheye cameras are discussed. The data and conditions required to replicate these features in a simulated environment are identified. Finally, a baseline traffic flow and traffic light phase model is created using camera data from the City of Hamilton. A DDDQN optimization algorithm used to reduce individual traffic light queue length and wait times is developed using the SUMO traffic simulator. The algorithm is trained over different maps and is then deployed onto a large map of various streets in the City of Hamilton. The algorithm is tested through a user-controlled driving simulator, observing excellent performance results over long routes.