随着智能网联车辆（CAV）技术在主动交通管理领域的深入应用，可变限速（VSL）策略成为提升道路交通流通行效率和安全性的关键。针对城市快速路合流区域的交通冲突导致通行能力下降和车速急剧变化问题，提出了一种面向车联网环境下快速路主线和入口匝道的协同可变限速控制策略方法。首先，采用基于Motorway Traffic Flow Network Model（METANET）的主线交通流预测模型，以总行程时间和距离最小构建双目标函数，通过应用模型预测控制（MPC）进行优化求解；然后，将可变限速控制问题抽象为马尔可夫决策过程，以平均速度、吞吐量和车均延误为指标构建复合奖励函数，通过引入深度Q网络（DQN）计算不同交通流状态下最优匝道限速，通过路侧设施（I2V）向CAV发布限速信息；最后，以徐州市北三环快速路为实例对提出的协同控制策略方法进行仿真测试。基于Simulation of Urban Mobility （SUMO）仿真实验结果表明：提出的策略方法与仅对主线进行限速控制的场景相比，路网车辆总行程时间减少8.51%，平均速度提升14.49%，交通密度波动降低14.81%，证明了该策略方法在车联网环境下能有效提升合流区通行效率，减小主线和入口匝道车辆的速度差异，缩小交通拥堵时空范围，从而提高城市快速路交通流的稳定性。

With the increasing application of Connected and Autonomous Vehicle (CAV) technologies in active traffic management, Variable Speed Limit (VSL) strategies have become crucial for improving traffic flow efficiency and safety. To address the issues of decreased traffic capacity and abrupt speed variations caused by traffic conflicts in urban expressway merging areas, a cooperative variable speed limit (VSL) control strategy was proposed for the mainline and on-ramp under a connected vehicle environment. Firstly, a mainline traffic flow prediction model based on Motorway Traffic Flow Network Modle (METANET) was adopted, constructing a bi-objective function to minimize the total travel time and distance, using Model Predictive Control (MPC). Then, the variable speed limit control problem was modelled as a Markov decision process, with a composite reward function based on average speed, throughput, and vehicle delay. By introducing Deep Q-network (DQN), the optimal on-ramp speed limits under different traffic flow conditions were calculated and disseminated to CAVs through Vehicle-to-Infrastructure (V2I) communication. Finally, the proposed coordinated control strategy was simulated and tested using the North Third Ring Expressway in Xuzhou, China as a case study. The empirical results based on SUMO microsimulation demonstrate that the proposed strategy, compared to the scenario with speed control only on the mainline, reduces the total travel time of network vehicles by 8.51%, increases the average speed by 14.49%, and reduces traffic density fluctuations by 14.81%. These results demonstrate that the proposed method can effectively improve traffic flow efficiency in merging areas under a connected vehicle environment, reduce speed differences between mainline and ramp vehicles, and shrink the spatiotemporal scope of congestion, thereby enhancing the stability of urban expressway traffic flow.