MULTISCALE CONTROL OF GENERIC SECOND ORDER TRAFFIC MODELS BY DRIVER-ASSIST VEHICLES

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 modeling 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 optimize 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 vehiclewise implementation of a driver-assist control to its optimal hydrodynamic design.

Automated summary

This study derives generic high order macroscopic traffic models from a follow-the-leader particle description via a kinetic approach, showing the optimization of global traffic trends through a hierarchical control structure. Numerical simulations investigate the effect of this control hierarchy in specific case studies, illustrating the multiscale path from vehiclewise implementation of driver-assist control to its optimal hydrodynamic design.