Scalable traffic stability analysis in mixed-autonomy using continuum models
Scalable traffic stability analysis in mixed-autonomy using continuum models
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DOI：
10.1016/j.trc.2020.01.007
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发表时间：
2020-02
影响因子：
8.3
通讯作者：
Kuang Huang;Xuan Di;Q. Du;Xi Chen
中科院分区：
文献类型：
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作者：
Kuang Huang;Xuan Di;Q. Du;Xi Chen
本文提出了可扩展的交通稳定性分析的纯连接和自动驾驶汽车（CAV）交通和混合交通的基础上连续交通流模型。人类驾驶车辆（HDV）采用非平衡交通流模型，即，Aw-Rascle-Zhang（ARZ）捕捉HDV流量的不稳定性。CAV建模的平均场游戏描述他们的非合作行为的理性效用优化代理。使用连续体模型有助于避免微观多类流量模型中的可扩展性问题。通过线性稳定性分析，证明了平均场博弈交通流模型与传统交通流模型的不同之处，并且只有当总密度在一定范围内时才能证明模型的稳定性.我们还表明，从数值实验，CAV有助于稳定混合交通。此外，我们量化的CAV的渗透率和控制器设计对交通稳定性的影响。研究结果可以为人类驾驶员和城市规划者提供关于混合自动驾驶中交通稳定性的定性见解。研究结果也为CAV制造商的CAV控制器设计提供了参考。
This paper presents scalable traffic stability analysis for both pure connected and autonomous vehicle (CAV) traffic and mixed traffic based on continuum traffic flow models. Human-drive vehicles (HDVs) are modeled by a non-equilibrium traffic flow model, i.e., Aw-Rascle-Zhang (ARZ) to capture HDV traffic's unstable nature. CAVs are modeled by a mean field game describing their non-cooperative behaviors as rational utility-optimizing agents. Working with continuum models helps avoiding scalability issues in microscopic multi-class traffic models. We demonstrate from linear stability analysis that the mean field game traffic flow model behaves differently from traditional traffic flow models and stability can only be proved when the total density is in a certain regime. We also show from numerical experiments that CAVs help stabilize mixed traffic. Further, we quantify the impact of CAV's penetration rate and controller design on traffic stability. The results may provide qualitative insights on traffic stability in mixed-autonomy for human drivers and city planners. The results also provide suggestions on CAV controller design for CAV manufacturers.