AI 中文总结
研究考虑交通组成不确定性的高速公路网络中CAV动态限速控制问题，提出含不确定性感知模型及交通组成感知MPC的框架，经仿真实验验证，该框架能生成更协调限速，减少行程时间并带来环境效益。
AI 中文摘要
动态限速控制已成为在联网自动驾驶车辆（CAV）的混合交通环境中提高高速公路可持续性的一种有前景的策略。然而，大多数现有方法假设CAV渗透率是确定性的且在整个控制范围内可准确得知。实际上，渗透率存在固有观测误差，导致混合交通组成不确定，进而降低控制性能。为克服此限制，本研究提出一种新颖的模型预测控制（MPC）框架，用于高速公路网络中CAV的动态限速控制，该框架在流量预测和控制优化中明确纳入交通组成不确定性。首先开发了一个不确定性感知宏观混合交通模型，其中不确定渗透率通过影响混合自由流速度、容量和容量下降条件，通过混合基本图传播到流动力学。然后，制定了一种交通组成感知MPC，针对多个可允许的渗透率实现优化CAV限速，从而提高异构交通条件下的控制鲁棒性。在单瓶颈高速公路走廊和具有合流-分流相互作用的多瓶颈高速公路网络上进行了仿真实验。结果表明，所提出的控制器生成了更具空间协调性的限速，有效减少了行程时间并带来环境效益。
英文摘要
Dynamic speed limit control has emerged as a promising strategy to improve freeway sustainability in mixed traffic environments with connected automated vehicles (CAVs). However, most existing approaches assume that the CAV penetration rate is deterministic and can be accurately known throughout the control horizon. In reality, the penetration rate has inherent observation errors, leading to uncertainty in mixed traffic composition, which in turn degrades control performance. To overcome this limitation, this study proposes a novel model predictive control (MPC) framework for dynamic CAV speed limit control in freeway networks that explicitly incorporates traffic composition uncertainty into both flow prediction and control optimization. An uncertainty-aware macroscopic mixed traffic model is first developed, where the uncertain penetration rate propagates through the mixed fundamental diagram to the flow dynamics by affecting the mixed free-flow speed, capacity, and capacity drop condition. Then, a traffic composition-aware MPC is formulated to optimize CAV speed limits against multiple admissible penetration rate realizations, thereby improving control robustness under heterogeneous traffic conditions. Simulation experiments are conducted on both a single-bottleneck freeway corridor and a multi-bottleneck freeway network with merge-diverge interactions. The results demonstrate that the proposed controller generates more spatially coordinated speed limits, which effectively reduce travel time spent and provide environmental benefits.
