When Altruism Meets Autonomy: Managing Bottleneck Congestion with Strategic Autonomous Vehicles

TL;DR

This paper presents a unified equilibrium framework to analyze and optimize mixed-autonomy traffic at highway weaving ramps, revealing how autonomous vehicles can strategically influence system performance amid selfish human drivers.

Contribution

It introduces a novel Stackelberg--Wardrop model capturing the strategic role of autonomous vehicles in mixed traffic and generalizes it to heterogeneous driver preferences.

Findings

AV impact is non-increasing with penetration under selfish behavior.

Equilibrium existence and uniqueness are established for the model.

Strategic AV control can induce system-level efficiency gains.

Abstract

Weaving ramps are critical bottlenecks in highway networks due to conflicting traffic flows and complex interactions among heterogeneous vehicle types. In mixed-autonomy settings, the presence of controllable autonomous vehicles (AVs) introduces new opportunities to influence system-level outcomes, yet the structural impact of such control remains poorly understood. This paper develops a unified equilibrium framework to capture, predict, and optimize aggregate lane-choice behavior in weaving ramps with heterogeneous vehicle populations. We first formulate a Wardrop-based model capturing the selfish behavior of human-driven vehicles (HDVs) and establish existence, uniqueness, and validity of the resulting equilibrium. We then introduce a Stackelberg--Wardrop formulation in which AVs act as strategic leaders optimizing system performance, while HDVs respond through equilibrium adaptation. The framework is further generalized to incorporate heterogeneous behavioral preferences of HDVs and AVs via a Social Value Orientation (SVO) model. Our analysis reveals a fundamental structural property of mixed-autonomy traffic systems: under selfish HDV behavior, the impact of AV penetration is inherently non-increasing, exhibiting plateau regions where performance remains unchanged and improves only at critical thresholds. These results provide principled guidance for the design of AV control and incentive mechanisms in the presence of selfish human behavior, and demonstrate how strategically controlled autonomous agents can be deployed to induce system-level efficiency gains in mixed-autonomy transportation networks.