面向混合自动驾驶交通的具有前视效应的各向异性交通流模型
An anisotropic traffic flow model with look-ahead effect for mixed autonomy traffic
AI总结:
该研究扩展 ARZ 模型,以非局部平均密度刻画 CAVs 前视效应,并通过扰动分析与数值实验揭示前视距离、渗透率及空间分布对混合交通稳定性的影响。
摘要:
本文扩展了 Aw-Rascle-Zhang(ARZ)非平衡交通流模型,以考虑网联自动驾驶车辆(CAVs)的前视能力,以及人工驾驶车辆与自动驾驶车辆的混合流动态。CAVs 的前视效应通过一定距离(前视距离)内的非局部平均密度来刻画。我们利用波扰动分析表明,增大前视距离会放宽稳定性判据。然而,数值实验显示,更长的前视距离并不一定会带来更快的平衡状态收敛。我们还考察了 CAVs 的空间分布和市场渗透率的影响,结果表明提高市场渗透率有助于稳定混合交通,而 CAVs 的空间分布对稳定性影响较小。这些结果揭示了利用 CAVs 稳定交通的潜力,并可为混合自动驾驶环境中的速度控制提供定性洞见。
英文摘要:
In this paper we extend the Aw-Rascle-Zhang (ARZ) non-equilibrium traffic flow model to take into account the look-ahead capability of connected and autonomous vehicles (CAVs), and the mixed flow dynamics of human driven and autonomous vehicles. The look-ahead effect of CAVs is captured by a non-local averaged density within a certain distance (the look-ahead distance). We show, using wave perturbation analysis, that increased look-ahead distance loosens the stability criteria. Our numerical experiments, however, showed that a longer look-ahead distance does not necessarily lead to faster convergence to equilibrium states. We also examined the impact of spatial distributions and market penetrations of CAVs and showed that increased market penetration helps stabilizing mixed traffic while the spatial distribution of CAVs have less effect on stability. The results revealed the potential of using CAVs to stabilize traffic, and may provide qualitative insights on speed control in the mixed autonomy environment.
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