提出了一种面向智能网联车辆（CAV）专用车道的柔性管理策略，在保证CAV在专用车道权益的同时，提升CAV市场渗透率（MPR）较低时的道路总体交通流量。构建了多车道场景下柔性管理策略的数学模型，基于CAV渗透率、队列强度、驾驶风格以及道路交通需求等参数，采用数值分析方法计算了不同车道管理策略在多个场景下的交通流量，并与典型的强制管理策略进行对比，同时在分析过程中考虑了普通车道上不同驾驶风格的人驾车辆（HDV）共存的情况。数值分析结果表明：在单向双车道中设置内侧为专用车道的场景下，当MPR小于50%时，随着交通需求从2000 veh/h增加至5000 veh/h，柔性管理策略下的道路交通流量始终优于强制管理策略，尤其是当MPR小于30%时，其优势更加明显，说明此时适合选择柔性管理策略；同时，柔性管理策略达到目标交通需求时所对应的渗透率区间范围更大，区间起点所对应的渗透率也更加提前，且CAV驾驶风格越激进，上述区间范围的区别越明显；而当MPR大于50%时，在相同的驾驶风格下，两种策略所对应的交通流量变化趋势基本相同。单向三车道场景下同样设置内侧为专用车道，其分析结果与双车道较为相似；进一步拓展至单向四车道场景时，讨论了设置一条和两条专用车道的情况，并给出了四种专用车道管理方案，从总体来看，柔性管理策略在中低渗透率下的表现仍优于强制管理策略。进一步基于SUMO在单向三车道场景下对两种专用车道管理策略进行仿真，验证了所提出车道管理策略的有效性。

A flexible management strategy for dedicated lanes of connected and automated vehicles (CAVs) is proposed to ensure CAVs priority while enhancing overall road traffic flow when the market penetration rate (MPR) of CAVs is relatively low. A mathematical model for the flexible management strategy is constructed under a multi-lane scenario, incorporating parameters such as CAV market penetration rate, queue intensity, driving style, and road traffic demand. Numerical analysis is conducted to evaluate traffic flow under different lane management strategies across multiple scenarios, comparing the proposed flexible strategy with a typical mandatory management strategy. The analysis also considers the coexistence of human-driven vehicles (HDVs) with varying driving styles in non-dedicated lanes. Numerical results indicate that when a one-way two-lane road is configured with the inner lane as a dedicated CAV lane, the flexible management strategy consistently outperforms the mandatory strategy in terms of road traffic flow when MPR is below 50%, particularly when MPR is below 30%. As traffic demand increases from 2000 veh/h to 5000 veh/h, the advantage of the flexible management strategy becomes more evident, suggesting its suitability under low MPR conditions. Additionally, the flexible management strategy supports a broader MPR range to achieve the target traffic demand, with an earlier starting point of the effective MPR interval. This difference becomes more pronounced as the driving style of CAVs becomes more aggressive. However, when MPR exceeds 50%, both strategies exhibit similar traffic flow trends under identical driving styles. For a one-way three-lane scenario with the inner lane designated as a dedicated lane, the analysis yields similar results to the two-lane scenario. Further extending the study to a four-lane scenario, both one and two dedicated lane configurations are examined, leading to four dedicated lane management schemes. Overall, the flexible management strategy remains superior to the mandatory strategy under low and medium penetration rates. Furthermore, simulations based on SUMO in a three-lane scenario validate the effectiveness of the proposed lane management strategies.