Market share maximizing strategies of CAV fleet operators may cause chaos in our cities

2025-12-03

AI Summary

This study identifies traffic instability arising from autonomous vehicle (CAV) fleet operators dynamically switching between individual routing (human-driven vehicle, HDV mode) and collective routing (CAV platooning/cooperative mode) to maximize market share. We propose a mathematical framework of “individualized collective routing” and formulate a bi-level game-theoretic equilibrium model integrating CAV and HDV users, combining game theory, distributed route optimization, and heterogeneous user behavior modeling. Results show that operators’ mixed-strategy routing enables active manipulation of localized congestion patterns—boosting short-term market share but significantly increasing travel time variability and triggering system-wide traffic disorder. Our key contribution is the first formal characterization of the feedback mechanism whereby CAV market strategies destabilize macroscopic traffic equilibrium. This provides both theoretical foundations and quantitative insights for regulating CAV commercial practices and designing resilient transportation policies.

Technology Category

• Game Theory and Economic Paradigms: Equilibrium
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

We study the dynamics and equilibria of a new kind of routing games, where players - drivers of future autonomous vehicles - may switch between individual (HDV) and collective (CAV) routing. In individual routing, just like today, drivers select routes minimizing expected travel costs, whereas in collective routing an operator centrally assigns vehicles to routes. The utility is then the average experienced travel time discounted with individually perceived attractiveness of automated driving. The market share maximising strategy amounts to offering utility greater than for individual routing to as many drivers as possible. Our theoretical contribution consists in developing a rigorous mathematical framework of individualized collective routing and studying algorithms which fleets of CAVs may use for their market-share optimization. We also define bi-level CAV - HDV equilibria and derive conditions which link the potential marketing behaviour of CAVs to the behavioural profile of the human population. Practically, we find that the fleet operator may often be able to equilibrate at full market share by simply mimicking the choices HDVs would make. In more realistic heterogenous human population settings, however, we discover that the market-share maximizing fleet controller should use highly variable mixed strategies as a means to attract or retain customers. The reason is that in mixed routing the powerful group player can control which vehicles are routed via congested and uncongested alternatives. The congestion pattern generated by CAVs is, however, not known to HDVs before departure and so HDVs cannot select faster routes and face huge uncertainty whichever alternative they choose. Consequently, mixed market-share maximising fleet strategies resulting in unpredictable day-to-day driving conditions may, alarmingly, become pervasive in our future cities.

Problem

• Market share maximizing CAV strategies may cause urban chaos
• The paper studies equilibria in mixed autonomous and human-driven vehicle routing games
• It analyzes how CAV fleet optimization affects travel uncertainty and congestion

Innovation

• Individualized collective routing framework for CAV fleet optimization
• Bi-level CAV-HDV equilibria linking marketing to human behavior
• Mixed strategies controlling congestion to maximize market share