Existing macroscopic traffic control methods often struggle to strictly regulate rare, safety-critical extreme events under stochastic disturbances. In this paper, we develop a rare chance-constrained optimal control framework for autonomous traffic management. To efficiently enforce these probabilistic safety specifications, we exploit a large deviation theory (LDT) based approximation method, which converts the original highly non-convex, sampling-heavy optimization problem into a tractable deterministic nonlinear programming problem. In addition, the proposed LDT-based reformulation exhibits superior computational scalability, as it maintains a constant computational burden regardless of the target violation probability level, effectively bypassing the extreme scaling bottlenecks of traditional sampling-based methods. The effectiveness of the proposed framework in achieving precise near-target probability control and superior computational efficiency over risk-averse baselines is illustrated through extensive numerical simulations across diverse traffic risk measures.

翻译：现有宏观交通控制方法在随机扰动下往往难以严格约束罕见的安全关键极端事件。本文构建了面向自主交通管理的稀有机会约束最优控制框架。为高效实现概率安全性规范，我们采用基于大偏差理论(LDT)的近似方法，将原始高度非凸、采样密集的优化问题转化为可解的确定性非线性规划问题。此外，所提出的LDT重构方法展现出优异的计算可扩展性——无论目标违反概率水平如何，其计算负荷保持恒定，有效规避了传统采样方法面临的极端扩展瓶颈。通过涵盖多种交通风险指标的广泛数值仿真，验证了该框架在实现精准近目标概率控制及超越风险规避基准方法的计算效率方面的有效性。