This paper presents a behavioral car following model, named the chained asymmetric behavior model, that improves on the asymmetric behavior model. This model is inspired by the empirical observation that vehicles react proportionately to the magnitude of disturbance experienced when traversing through a stop-and-go oscillation, deviating from a constant following behavior observed in equilibrium conditions. Findings from simulation experiments suggest that this “second-order” effect significantly affects traffic throughput and evolution under disturbances. Knowledge obtained from the model is leveraged toward designing control for connected automated vehicles in mixed traffic streams.

本文提出了一种行为汽车跟随模型，称为链式不对称行为模型，该模型对不对称行为模型进行了改进。该模型的灵感来自于经验性观察，即车辆经过停走振动后，会与所经历的扰动幅度成比例地做出反应，这与在平衡条件下观察到的恒定跟随行为背道而驰。仿真实验的结果表明，这种“二阶”效应会显着影响交通量和扰动下的演进。从模型中获得的知识可用于设计混合交通流中的互联自动车辆的控制。