Agent-Based Evolutionary Dynamics for Mixed Autonomy Weaving Ramps

2026-09-28

AI Summary

This study addresses the unclear emergence mechanisms of altruistic behavior at weaving ramps in mixed-autonomy traffic by developing an agent-based evolutionary game model, wherein vehicles adaptively select lanes through altruistic objectives. This work provides the first micro-level explanation for the classical Wardrop equilibrium, rigorously proving that decentralized dynamics converge to a unique equilibrium while quantitatively analyzing the effects of heterogeneous preferences and incomplete information. Simulations demonstrate strong consistency between microscopic outcomes and macroscopic predictions, revealing convergence rates and the distribution patterns of altruistic burden. These findings offer theoretical support for the deployment of mixed-autonomy transportation systems.

Abstract

Existing models of mixed-autonomy weaving ramps characterize how altruistic connected and automated vehicles (CAVs) can improve traffic efficiency at the population level, but provide limited insight into how such behavior emerges from decentralized vehicle interactions or how it is affected by finite populations, heterogeneous preferences, and imperfect information. We develop an agent-based model of a macroscopic weaving-ramp framework in which individual vehicles adapt their lane choices using an evolutionary game-theoretic update rule and altruism-based objectives providing a microscopic interpretation of the original Wardrop model. We prove convergence of the decentralized dynamics to the unique equilibrium predicted by the macroscopic theory. Beyond reproducing aggregate equilibrium behavior, the framework enables the study of deployment-level questions that cannot be addressed by static analysis. Simulation results demonstrate close agreement with the macroscopic predictions while revealing how convergence rates, adaptation to changing traffic conditions, heterogeneous altruism levels among CAVs, and imperfect state information influence system performance and the distribution of altruistic burden across vehicles. These results provide a bridge between equilibrium traffic theory and decentralized mixed-autonomy deployment.

Problem

• mixed autonomy
• weaving ramps
• evolutionary dynamics
• connected and automated vehicles
• decentralized interactions

Innovation

• Agent-based model
• Evolutionary game theory
• Mixed autonomy
• Weaving ramps
• Decentralized dynamics