论文标题
交通流量的随机因素和弦稳定性：基于汽车跟随模型的分析研究和数值研究
Stochastic factors and string stability of traffic flow: Analytical investigation and numerical study based on car-following models
论文作者
论文摘要
交通不稳定的出现动力一直引起人们的特别关注。数十年来，研究人员使用确定性交通模型研究了交通流量的稳定性，更少强调了随机因素的存在。但是，最近的经验和理论发现表明，随机因素倾向于破坏交通流量并刺激交通振荡的凹生长模式。在本文中，我们通过广义Lyapunov方程的平均值得出了通用随机连续遵循模型的弦稳定性条件。实际上，我们发现随机性的存在破坏了交通流量。随机性的影响取决于对间隙的敏感性和对速度差异的敏感性。已经对三个典型的汽车跟随模型进行了数值模拟，以验证我们的理论分析。最后，我们针对经验数据校准并验证了随机汽车跟随模型。发现随机汽车跟踪模型再现了观察到的交通不稳定，并捕获了交通振荡的凹生长模式。我们的结果在理论和数字上进一步强调了随机因素对交通动态有重大影响。
The emergence dynamics of traffic instability has always attracted particular attention. For several decades, researchers have studied the stability of traffic flow using deterministic traffic models, with less emphasis on the presence of stochastic factors. However, recent empirical and theoretical findings have demonstrated that the stochastic factors tend to destabilize traffic flow and stimulate the concave growth pattern of traffic oscillations. In this paper, we derive a string stability condition of a general stochastic continuous car-following model by the mean of the generalized Lyapunov equation. We have found, indeed, that the presence of stochasticity destabilizes the traffic flow. The impact of stochasticity depends on both the sensitivity to the gap and the sensitivity to the velocity difference. Numerical simulations of three typical car-following models have been carried out to validate our theoretical analysis. Finally, we have calibrated and validated the stochastic car-following models against empirical data. It is found that the stochastic car-following models reproduce the observed traffic instability and capture the concave growth pattern of traffic oscillations. Our results further highlight theoretically and numerically that the stochastic factors have a significant impact on traffic dynamics.
