AI 中文总结
该研究提出结合COMF理论与两站点簇方法的改进平均场分析，用于单车道交通流元胞自动机模型，可更精准捕捉关联与相分离特性，经VDR模型验证了其精度与物理洞察。
AI 中文摘要
我们提出了针对单车道车辆交通元胞自动机模型的改进型平均场分析方法。结合此前已成功应用于类似模型的面向车辆平均场（Car-Oriented-Mean-Field, COMF）理论与两站点簇方法的相关特性，旨在更精准地捕捉短程与长程关联。与经典平均场理论不同，该改进方法适用于具有非均匀定态的模型，能够捕捉相分离的基本特性，例如含慢启动规则的模型中的相分离。通过将其应用于最大速度v_max=1的VDR模型，可验证其改进的精度与新的物理洞察。
英文摘要
We propose an improved mean-field analysis of cellular automata models of single-lane vehicular traffic. By combining aspects of the Car-Oriented-Mean-Field (COMF) theory and the 2-site cluster method, which have been previously successfully applied to similar models, we aim to capture both short- and long-range correlations more accurately. In contrast to classical mean-field theories, the improved method is well suited for models with inhomogeneous stationary states and able to capture the essential properties of phase separation, e.g. in models with slow-to-start rules. The improved accuracy and new physical insights are illustrated through an application to the VDR model with $v_{\text{max}}=1$.
