Congestion Mitigation in Vehicular Traffic Networks with Multiple Operational Modalities

2026-01-08

arXiv.org

AI Summary

This study addresses the challenge of selfish routing and exacerbated congestion arising from heterogeneous perception capabilities and route preferences among multimodal vehicles—encompassing manual driving, driver assistance, teleoperation, and full autonomy. For the first time, it integrates vehicles capable of dynamically switching operational modes into a traffic flow management framework by formulating a repeated single-leader multi-follower Stackelberg game with asymmetric information. The work proposes TACTS, a novel strategy that jointly incorporates trust-awareness and control allocation. TACTS employs a regret-matching-based adaptive mixed-strategy update mechanism and demonstrates significant reductions in total network travel time across diverse real-world road networks and high-congestion scenarios, outperforming existing route planning and control allocation approaches.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Multiagent Systems: Mechanism Design
• Intelligent Robots: Multimodal Perception & Sensor Fusion

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
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Modern commercial ground vehicles are increasingly equipped with multiple operational modalities (e.g., human driving, advanced driver assistance, remote tele-operation, full autonomy). These often rely on heterogeneous sensing infrastructures and distinct routing algorithms, which can yield misaligned perceptions of the traffic environment and route preferences. While such technologies accelerate the transition toward increasingly intelligent transportation networks, their current deployment fails to avoid challenges associated with selfish routing behavior, in which drivers or automated agents prioritize individually optimal routes instead of network-wide congestion mitigation. Existing traffic flow management strategies can address leader-follower dynamics in traffic routing problems but are not designed to account for vehicles capable of dynamically switching between multiple operational modes. This paper models the interaction between a vehicle control arbitration system and a multi-modal vehicle as a repeated single-leader, multiple follower Stackelberg game with asymmetric information. To address the intractability of computing an exact solution in this setting, we propose a Trust-Aware Control Trading Strategy (TACTS) utilizing a regret matching-based algorithm to adaptively update the arbitration system's mixed strategy over sequential, dynamic routing decisions. Theoretical results provide bounds on the realized total network travel time under TACTS algorithm relative to the system-optimal total network travel time. Experimental results of simulations between the system and a vehicle in several real-world traffic networks under various different congestion levels demonstrate that TACTS consistently reduces network-wide congestion and generally outperforms alternative routing and control-allocation strategies, particularly under high congestion and heavy induced vehicle flows.

Problem

• congestion mitigation
• multi-modal vehicles
• selfish routing
• traffic networks
• operational modalities

Innovation

• multi-modal vehicles
• Stackelberg game
• regret matching
• congestion mitigation
• adaptive control arbitration