Stability Analysis Framework of Traffic Control Algorithms

The paper explores the resilience of traffic management systems to shock situations, focusing on the application of agent-based modeling of a traffic system and the effectiveness of reinforcement learning algorithms in adapting to such conditions. Through simulation, it is analyzed how these algorithms can maintain or quickly recover system stability, ensuring effi-cient traffic flow even under unexpected changes. The research provides insights into the potential of reinforcement learning as a tool for dynamic system management, offering a promising approach to optimizing a performance in urban traffic networks as long as simulating such networks.