The merging zone of urban road system is a place with high incidence of traffic congestion. Due to the absence of signal lights in the confluence area, the self-organized driving of vehicles will lead to the situation of "cut in". Such uncivilized driving behavior is likely to lead to the occurrence of traffic accidents. The traditional solution is to control the traffic flow in the confluence area. Considering the driving behavior, this paper introduces the social credit mechanism to study the traffic efficiency and fairness of the confluence area under the driver's self-organizing behavior. The development of intelligent transportation lays the foundation for a Drive Order Recommend System (DORS) based on social credit, which will enable the emergence of cooperation in urban road confluence areas. Based on a simple Y-shaped confluence area, we establish an agent-based-model(ABM) to evolve the driver's driving behavior. The agent's behavior will be influenced by the action rules and evaluation rules, and it will be strengthened according to the previous experience. The result shows that after the introduction of social credit, the cooperation rate of the system will be improved without reducing the original traffic efficiency. These findings will be applied to the driving order recommendation system based on human behavior for automated road users, and will also lay a foundation for improving the credit information system in the field of transportation.

CICTP