With the increasing complexity of urban traffic flow, intersections have become critical bottlenecks for improving overall traffic efficiency. In previous research, we established a modeling framework for multivehicle conflict decoupling and proposed a traffic optimization decision-making method for intersection scenarios. In this study, we present the experimental results of multivehicle conflict decoupling modeling and traffic optimization decision-making methods. The proposed method is implemented on the miniature testbed of Tsinghua Research Center for Intelligent and Connected Vehicles and Transportation to validate its effectiveness, and the implementation details are thoroughly presented. Experimental results demonstrate that in a single experiment, the proposed multivehicle conflict decoupling modeling framework outperforms the benchmark algorithm in average travel time, total delay, and overall traffic uniformity. In the mixed traffic environment, the average number of laps and average speed are higher, and the standard deviation is lower, than those under fixed-time traffic signal control and have significantly reduced the travel delay, providing a theoretical basis for collaborative decision-making in mixed traffic. These experimental findings demonstrate the significant potential of the multivehicle conflict decoupling modeling framework and traffic optimization decision-making method. The approach can be applied to intersection scenarios with different penetration rates of intelligent connected vehicles (ICVs), enabling effective conflict resolution, enhancing traffic safety, and improving overall traffic efficiency.