Optimization-based incentivization and control scheme for autonomous traffic

TL;DR

This paper develops an optimal control framework using incentivization to improve traffic flow of autonomous vehicles, employing transport theory and PDEs, with numerical results showing the effectiveness of multiscale-norm penalties.

Contribution

It introduces a novel optimization algorithm for incentivizing autonomous vehicles to achieve uniform traffic distribution, integrating PDE-based dynamics and multiscale penalties.

Findings

Multiscale-norm cost optimization effectively improves traffic distribution.

L^2 cost optimization is less effective for traffic control.

Dedicated lanes may enhance incentivization strategies.

Abstract

We consider the problem of incentivization and optimal control of autonomous vehicles for improving traffic congestion. In our scenario, autonomous vehicles must be incentivized in order to participate in traffic improvement. Using the theory and methods of optimal transport, we propose a constrained optimization framework over dynamics governed by partial differential equations, so that we can optimally select a portion of vehicles to be incentivized and controlled. The goal of the optimization is to obtain a uniform distribution of vehicles over the spatial domain. To achieve this, we consider two types of penalties on vehicle density, one is the $L^2$ cost and the other is a multiscale-norm cost, commonly used in fluid-mixing problems. To solve this non-convex optimization problem, we introduce a novel algorithm, which iterates between solving a convex optimization problem and propagating the flow of uncontrolled vehicles according to the Lighthill-Whitham-Richards model. We perform numerical simulations, which suggest that the optimization of the $L^2$ cost is ineffective while optimization of the multiscale norm is effective. The results also suggest the use of a dedicated lane for this type of control in practice.