打破交通流中的行为一致性：最优协同缓冲的解析推导与数值验证
Breaking Behavioral Uniformity in Traffic Flow: Analytical Derivation and Numerical Verification of Optimal Cooperative Buffering
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中文总结 AI 辅助
本文针对交通流中的走走停停波问题，提出最优协同缓冲策略，通过解析推导与数值验证表明，该策略可扩展弦稳定参数空间、消散走走停停波并提升交通流量。
中文摘要 AI 辅助
走走停停（SGW）波是交通流理论中持续存在的挑战，严重损害高速公路的安全性、运营效率和环境可持续性。本文基于网联车辆可协同并承担不同角色的前提，数学推导了一种最优协同驾驶策略，由此背离了经典的车辆行为一致性假设。通过受线性稳定性条件约束的严格优化公式，解析证明了当指定的单个“缓冲”车辆保持更大的车头时距以吸收扰动，其余车辆形成紧密排列、高容量的车队时，交通系统的通行能力将实现全局最大化。该解析推导的最优解通过使用智能驾驶员模型（Intelligent Driver Model）和全速度差模型（Full Velocity Difference Model）进行的数值谱分析及受稳定性约束的通行能力优化得到验证。结果表明，这种非均匀协同缓冲策略具有双重优势：它从根本上扩展了弦稳定参数空间以主动消散走走停停波，同时与无缓冲交通及非协同的拥堵吸收驾驶策略相比，实现了显著更高的交通流量。
英文摘要
Stop-and-go waves pose a persistent challenge in traffic flow theory, significantly compromising highway safety, operational efficiency, and environmental sustainability. This paper mathematically derives an optimal cooperative driving policy based on the premise that connected vehicles can cooperate and assume distinct roles. In doing so, we depart from the classical assumption of uniform vehicular behavior. Through a rigorous optimization formulation constrained by linear stability conditions, it is analytically proved that the traffic system's throughput is globally maximized when a single designated "buffer" vehicle maintains an enlarged headway to absorb perturbations, enabling the remaining vehicles to form a tightly spaced, high-capacity platoon. This analytically derived optimum is confirmed via numerical spectral analysis and stability-constrained flow optimization using the Intelligent Driver Model and the Full Velocity Difference Model. The results demonstrate that this non-uniform cooperative buffering strategy yields dual benefits. It radically expands the string-stable parameter space to actively dissipate SGWs, while simultaneously achieving significantly higher traffic flow compared to both unbuffered traffic and non-cooperative Jam-Absorption Driving strategies.
