With the rapid development of urban transportation, traffic management at road intersections becomes increasingly complex. Improving the efficiency of road intersection traffic becomes an important research direction in the field of traffic management. Therefore, this thesis discusses the expression and reasoning method of traffic management knowledge at intersection. Firstly, based on the analysis of relevant literature and cases in the field of traffic management, the basic knowledge and specific application scenarios of road intersection traffic management are summarized. On this basis, the design framework for the expression of traffic management knowledge based on first-order logic is proposed, and the vocabulary of road traffic knowledge is established to classify traffic management knowledge and translated into first-order logic formula one by one. Secondly, this thesis complements the reasoning of traffic knowledge through inductive-deductive reasoning method, the reasoning goal into a conjunctive normal form and constantly summed up. Thus, new traffic management knowledge can be deduced and the function of knowledge base can be increased. Finally, through the joint compilation environment of Python and Prolog, the knowledge base simulation interactive interface is designed, and experiments are carried out on four different simulation interactive environments at road intersections. The calculated query accuracy rate of the knowledge base is above 97%, effectively verifying the reliability of the proposed knowledge base. This research results of this thesis can provide a new way of thinking and method for traffic management decision-making, and contribute to improving the efficiency of road intersections and promoting the sustainable development of urban traffic.