Abstract: The formation and propagation mechanisms of traffic oscillations are significantly influenced by the heterogeneity in driving behavior. By integrating the dual parameters of driver reaction ability and driving behavior asymmetry, the classic car following model was improved, and an asymmetric car-following model was constructed to reveal the micro-evolutionary patterns of traffic oscillations. Stability analysis indicates that traffic flow instability phenomena occur within specific ranges of speed and headway parameters. Simulation experiments further identify three kinds of triggering conditions for oscillations: traffic flow parameters entering the theoretical instability zone, initial headways of aggressive drivers being unable to match their speed requirements within the stable range as well as mismatch between speed and headway parameters for ordinary or conservative drivers within the stable range. Quantitative research based on the standard deviation of speed shows that the oscillation propagation speed of conservative driver fleets at low speeds is faster than that of aggressive drivers and the oscillation caused by the disturbance of the headway shows a concave growth. While the growth pattern caused by speed disturbance occurs differentiated growth patterns among driver types—exponential growth for aggressive and ordinary drivers, and linear growth for conservative drivers. When initial speed and headway achieve the parameter matching state, the propagation of oscillations is significantly suppressed or even blocked. This study establishes a quantitative mapping relationship between driving behavior characteristic parameters and the evolutionary patterns of oscillations, confirming that parameter matching can reduce the risk of oscillation propagation, which provides theoretical support for the design of cooperative control strategies for intelligent connected vehicles, especially in areas such as adaptive cruise control system parameter optimization and enhancement of the stability of mixed traffic flow.

Key words: traffic engineering; formation and propagation conditions; car-following model; traffic oscillations; driver behavior; asymmetry

摘要： 交通震荡的形成与传播机制受驾驶行为异质性影响显著。通过融合驾驶员反应能力和驾驶行为非对称性双参数改进经典跟驰模型，构建了非对称跟驰模型并对交通震荡微观演化规律进行了揭示。稳定性分析表明：交通流失稳现象出现在特定速度与车头间距参数区间，仿真实验进一步识别出3类震荡触发条件：交通流参数进入理论失稳区间；稳定区间内激进型驾驶员初始车头间距无法匹配速度需求；稳定区间内普通型或保守型驾驶员存在速度-车头间距参数失配。基于速度标准差的量化研究发现：低速状态下保守型驾驶员车队的震荡传播速度比激进型快，车头间距扰动引发的震荡呈凹型增长，而速度扰动导致的增长模式存在驾驶人类型分化——激进型与普通型呈指数增长，保守型则为线性增长。当初始速度与车头间距达到参数匹配状态时，震荡传播被显著抑制甚至阻断。研究建立了驾驶行为特征参数与震荡演化规律的量化映射关系，证实参数匹配可使震荡传播风险降低，为智能网联车辆协同控制策略设计提供了理论支撑，特别是在自适应巡航系统参数优化、混合交通流稳定性提升等领域具有直接应用价值。

关键词: 交通工程；形成传播条件；跟驰模型；交通震荡；驾驶人行为；非对称性