Vehicle automation, connectivity, artificial intelligence (AI), and advanced sensing enable new approaches to intersection management. This project investigates a virtual traffic light (VTL) framework that over time can replace conventional signalized intersections, which carry high capital, maintenance, and operating costs. In the proposed system, connected and automated vehicles (CAVs), along with connected pedestrians and bicyclists, communicate continuously with a virtual control agent that maintains real-time estimates of their positions, speeds, and intended trajectories. Using these data, the agent computes individualized right-of-way assignments—equivalent to virtual green, yellow, and red indications—and transmits them directly via vehicle-to-everything (V2X) communication to each user. By coordinating movements through communication rather than fixed infrastructure, the system can improve traffic efficiency, reduce delays, enhance safety, and lower costs. An additional advantage of VTL systems is their flexibility: control logic and policies can be updated through software, enabling rapid, low-cost adaptation compared with conventional traffic signals. Because full connectivity will be achieved gradually, the research first establishes upper-bound performance under ideal, fully connected conditions without failures, providing quantitative benchmarks. It then develops a roadmap for transitioning from today’s traffic control to increasingly connected environments, accommodating mixed traffic with varying levels of connectivity. The results will guide phased deployment strategies aligned with USDOT priorities in safety, mobility, sustainability, and cost-effective infrastructure modernization.