When Competition Helps: Achieving Optimal Traffic Flow with Multiple Autonomous Planners

TL;DR

This paper demonstrates that competition among autonomous vehicle planners can lead to optimal traffic flow, overcoming inefficiencies of selfish routing, through a novel routing mechanism that converges to the social optimum.

Contribution

It introduces a new routing mechanism that leverages competition among multiple autonomous planners to achieve optimal traffic flow, addressing a key challenge in traffic management.

Findings

Competition among planners can eliminate the Price of Anarchy.

The proposed mechanism converges to the optimal traffic assignment.

Validated on the classical Pigou network as a foundational case.

Abstract

The inefficiency of selfish routing in congested networks is a classical problem in algorithmic game theory, often captured by the Price of Anarchy (i.e., the ratio between the social cost of decentralized decisions and that of a centrally optimized solution.) With the advent of autonomous vehicles, capable of receiving and executing centrally assigned routes, it is natural to ask whether their deployment can eliminate this inefficiency. At first glance, a central authority could simply compute an optimal traffic assignment and instruct each vehicle to follow its assigned path. However, this vision overlooks critical challenges: routes must be individually rational (no vehicle has an incentive to deviate), and in practice, multiple planning agents (e.g., different companies) may coexist and compete. Surprisingly, we show that such competition is not merely an obstacle but a necessary ingredient for achieving optimal outcomes. In this work, we design a routing mechanism that embraces competition and converges to an optimal assignment, starting from the classical Pigou network as a foundational case.