Infrastructure-Based Hierarchical Control Design for Congestion Management in Heterogeneous Traffic Networks

Abstract

This paper develops a hierarchical mainstream traffic flow control for a heterogeneous traffic network with an unknown downstream bottleneck. A distributed extremum-seeking control approach is employed at the higher level to determine the optimal density of Autonomous Vehicles (AVs) and Human-Driven Vehicles (HDVs) in the controlled cells, considering unknown disturbances in the heterogeneous traffic network. At the lower level, a distributed filtered feedback linearization controller is designed to update the suggested velocity communicated to the AVs and HDVs so that the desired density determined at the higher level can be achieved in each cell. Furthermore, to model the heterogeneous traffic network, a multi-class METANET model is adopted to represent the aggregated behavior of the network. It is shown that the designed distributed extremum-seeking filtered feedback linearization controller can achieve the desired closed-loop performance despite the presence of unknown disturbances in the system.