How many autonomous vehicles are required to stabilize traffic flow?

TL;DR

This paper determines the minimum number of autonomous vehicles needed to stabilize traffic flow under realistic control constraints, using nonlinear optimization and simulations to inform AV deployment strategies.

Contribution

It introduces a method to compute the optimal lower bound on AV penetration rate considering control parameter bounds, extending prior unconstrained analyses.

Findings

Optimal AV penetration rate depends on control bounds.

Constrained scenarios require more AVs for stabilization.

Larger control constraints slow transient response.

Abstract

The collective behavior of human-driven vehicles (HVs) produces the well-known stop-and-go waves potentially leading to higher fuel consumption and emissions. This paper investigates the stabilization of traffic flow via a minimum number of autonomous vehicles (AVs) subject to constraints on the control parameters aiming to reduce the number of vehicles on the road while achieving lower fuel consumption and emissions. The unconstrained scenario has been well-studied in recent studies. The main motivation to investigate the constrained scenario is that, in realistic engineering applications, lower and upper bounds exist on the control parameters. For the constrained scenario, we optimally find the minimum number of required AVs (via computing the optimal lower bound on the AV penetration rate) to stabilize traffic flow for a given number of HVs. As an immediate consequence, we conclude that for a given number of AVs, the number of HVs in the stabilized traffic flow may not be arbitrarily large in the constrained scenario unlike the unconstrained scenario studied in the literature. We systematically propose a procedure to compute the optimal lower bound on the AV penetration rate using nonlinear optimization techniques. Finally, we validate the theoretical results via numerical simulations. Numerical simulations suggest that enlarging the constraint intervals makes a smaller optimal lower bound on the AV penetration rate attainable. However, it leads to a slower transient response due to a dominant pole closer to the origin.