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

2025-08-09

AI Summary

This paper addresses the challenge of achieving socially optimal traffic assignment in autonomous vehicle networks with multiple heterogeneous planners (e.g., competing OEMs), subject to individual rationality constraints. We propose a novel “competition-is-beneficial” paradigm, proving that moderate competition is *necessary* for attaining social-optimal routing—challenging the traditional dichotomy between centralized optimization and fully decentralized selfish routing. Leveraging algorithmic game theory, we design a distributed routing mechanism that guarantees individual rationality while minimizing system-wide cost. Through Price-of-Anarchy (PoA) analysis and multi-agent adaptive dynamics, we rigorously establish convergence to the social-cost-minimizing solution on canonical benchmarks including the Pigou network. Our mechanism eliminates inefficiencies induced by selfish routing yet remains compatible with real-world competitive multi-stakeholder architectures. It provides a scalable, incentive-compatible, and practically implementable framework for next-generation intelligent transportation systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

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.

Problem

• Address inefficiency in selfish routing for congested networks
• Ensure individually rational routes for autonomous vehicles
• Achieve optimal traffic flow with competing planners

Innovation

• Embraces competition for optimal routing
• Ensures individually rational route assignments
• Converges to optimal traffic flow