# Stabilizing Traffic via Autonomous Vehicles: A Continuum Mean Field Game Approach

## TL;DR

This paper introduces a continuum mean field game approach to analyze and enhance traffic stability in mixed autonomous and human-driven vehicle traffic, providing insights for vehicle design and urban planning.

## Contribution

It develops a novel mean field game model for AVs within continuum traffic flow, integrating it with non-equilibrium human vehicle models to analyze stability.

## Key findings

- AVs improve traffic stability in mixed traffic scenarios.
- Higher AV penetration rates lead to increased stability.
- Controller design significantly impacts traffic stability.

## Abstract

This paper presents scalable traffic stability analysis for both pure autonomous vehicle (AV) traffic and mixed traffic based on continuum traffic flow models. Human vehicles are modeled by a non-equilibrium traffic flow model, i.e., Aw-Rascle-Zhang (ARZ), which is unstable. AVs are modeled by the mean field game which assumes AVs are rational agents with anticipation capacities. It is shown from linear stability analysis and numerical experiments that AVs help stabilize the traffic. Further, we quantify the impact of AV's penetration rate and controller design on the traffic stability. The results may provide insights for AV manufacturers and city planners.