论文标题
驾驶员辅助车辆对通用二阶交通模型的多尺度控制
Multiscale control of generic second order traffic models by driver-assist vehicles
论文作者
论文摘要
我们通过动力学方法研究了从后面领导的粒子描述中研究通用高级宏观交通模型的推导。首先，我们将三阶流量模型恢复为恩斯科类动力学方程的流体动力极限。接下来，我们在车辆相互作用中介绍二进制控制对驾驶员辅助车辆提供的自动反馈进行建模，并通过另一个基于恩斯科格的水力动力学极限来对这样的新粒子描述进行更大的变化。所得的宏观模型现在是一个通用二阶模型（GSOM），它依次包含从微观相互作用继承的控制项。我们表明，可以选择这样的控制，以优化受GSOM动力学约束的全球交通趋势，例如车辆通量或道路拥堵。通过数值模拟，我们在某些特定的案例研究中研究了该控制层次结构的效果，从车辆依靠车辆实施驱动程序控制到其最佳流体动力设计，这示例了多尺度路径。
We study the derivation of generic high order macroscopic traffic models from a follow-the-leader particle description via a kinetic approach. First, we recover a third order traffic model as the hydrodynamic limit of an Enskog-type kinetic equation. Next, we introduce in the vehicle interactions a binary control modelling the automatic feedback provided by driver-assist vehicles and we upscale such a new particle description by means of another Enskog-based hydrodynamic limit. The resulting macroscopic model is now a Generic Second Order Model (GSOM), which contains in turn a control term inherited from the microscopic interactions. We show that such a control may be chosen so as to optimise global traffic trends, such as the vehicle flux or the road congestion, constrained by the GSOM dynamics. By means of numerical simulations, we investigate the effect of this control hierarchy in some specific case studies, which exemplify the multiscale path from the vehicle-wise implementation of a driver-assist control to its optimal hydrodynamic design.
