Urban traffic congestion is one of the main challenges for modern cities, necessitating highly adaptive and effective control strategies in complex and dynamic environments. This study explores the application of distributed auto epistemic reasoning (DAR) to symbolic control of urban traffic congestion. Modeling urban congestion through DAR involves representing the knowledge, beliefs, and reasoning processes of multiple autonomous agents (such as traffic control units, intelligent vehicles, and individual drivers) within a distributed system. These agents reason about their own information while synchronizing with others in the system. By facilitating seamless communication and coordination between these distributed agents, DAR improves the quality of collective decision-making that is crucial to effectively managing traffic flows. This approach is particularly relevant in contexts where urban congestion often involves ambiguous and variable information. Activating symbolic regulation strategies to control intersections and alleviate congestion is a critical component of the DAR model. Integrating DAR into symbolic intersection regulation enables the development of an adaptable and responsive system that continually improves urban traffic fluidity, reduces wait times, and strengthens overall city mobility.