Andrew Panyin Vormawor

The researcher has focused on advancing methodologies for dynamic traffic modeling and optimization, particularly in addressing real-world challenges such as congestion and driver behavior. Their work encompasses developing innovative models for multi-objective traffic flow control using machine learning, designing optimal strategies for vehicle-to-vehicle communication in dense networks, and exploring efficient solutions for real-time optimization through reinforcement learning and network congestion management. The overarching goal is to enhance traffic efficiency and reduce its negative impacts on urban mobility.