Macroscopic flow control of connected and automated vehicles at signalized intersections

To fully leverage connected automated vehicle (CAV) technology for improving traffic flow at signalized intersections, this paper addresses the scalability limitations of traditional microscopic control methods. We propose a macroscopic connected automated flow control (CAFC) framework based on the cell transmission model (CTM), which formulates the vehicle sorting problem as a computationally efficient Mixed-Integer Quadratically Constrained Program (MIQCP). Numerical experiments, comparing our CAFC strategy against a traditional dedicated-lane benchmark, demonstrate a throughput improvement of approximately 63%. The framework also shows strong robustness in dynamic scenarios with mismatched traffic demand and signal timings, consistently outperforming a stronger, demand-responsive baseline. The results indicate that macroscopic flow control offers a scalable and highly effective alternative to microscopic methods for real-time traffic management in pure CAV environments.