Smart Traffic Manager for Speed Harmonization and Stop-and-Go Waves Mitigation Dedicated to Connected Autonomous Vehicles

Abstract

The present article introduces a smart traffic manager, conceived as an algorithm tasked with supervising vehicles. It gathers and disseminates macroscopic traffic flow data among Connected Autonomous Vehicles (CAVs) to facilitate their attainment of String Stability (SS). This method combines machine-learning techniques and physics-based traffic flow models, establishing a crucial connection between microscopic and macroscopic modeling levels. It enables CAVs to utilize mesoscopic controllers that effectively mitigate stop-and-go waves while ensuring speed harmonization, thereby proving their Disturbance String Stability (DSS). Simulation results demonstrate the efficacy of this proposed solution.