Making full use of real-time feedback traffic information to guide road traffic is one of the hot spots in the field of intelligent transportation system, which has practical significance for improving travel efficiency and reducing congestion. This paper proposes a new average speed feedback strategy based on real-time information, and uses it to improve the MCD model. The fundamental diagram, space–time diagram and flow interruption effect diagram obtained by simulation show that the results are consistent with the three-phase flow theory. It is found that in the free flow phase, the mean velocity feedback enables vehicles to adjust the velocity, accelerate the dissipation of the deceleration wave and maintain the stability of the traffic flow. In contrast, in the synchronized flow phase, the mean velocity feedback strategy keeps the traffic flow constant, reduces the velocity disturbance caused by the drivers’ own factors and improves the road operation efficiency.

充分利用实时反馈交通信息来指导道路交通是智能交通系统领域的热点之一，对于提高出行效率，减少交通拥堵具有现实意义。本文提出了一种基于实时信息的平均速度反馈策略，并将其用于改进MCD模型。通过仿真得到的基本图，时空图和流动中断效果图表明，结果与三相流理论相吻合。发现在自由流动阶段，平均速度反馈使车辆能够调节速度，加速减速波的耗散并保持交通流的稳定性。相反，在同步流阶段，