Decentralized Multi-Agent Urban Traffic Management via Spatio-Temporal Mobility Profile Planning

2026-08-08

AI Summary

This work addresses the challenge of simultaneously achieving system efficiency, low communication overhead, safe execution, and scalability in large-scale urban traffic management. To this end, we propose VeloCity, a decentralized spatiotemporal trajectory planning framework that, for the first time, supports arbitrary complex urban road networks. In VeloCity, each connected autonomous vehicle autonomously generates conflict-free, dynamically feasible trajectories that minimize travel time, based on a spatiotemporal slot reservation table provided by local coordinators. The approach requires no scenario-specific customization and integrates distributed spatiotemporal profile optimization with a generic road topology adaptation mechanism. Large-scale simulations in Tokyo, Manhattan, Rome, and Bologna demonstrate significant reductions in both travel time and delay variance, effectively prevent gridlock, and exhibit exceptional scalability and performance advantages.

Abstract

As modern cities face increasingly severe traffic congestion, connected and autonomous vehicles (CAVs) have emerged as a crucial enabling technology for next-generation intelligent traffic management. However, fully realizing this potential is hindered by the limitations of current paradigms. Existing approaches typically optimize localized interactions rather than system-wide efficiency, incur severe communication overhead, or lack the deterministic guarantees required for safe kinematic execution. Furthermore, current multi-agent adaptations are frequently restricted to small predefined scenarios, failing to scale across large and complex urban networks. To bridge this gap, this paper introduces VeloCity, a decentralized multi-agent spatio-temporal mobility profile planning framework designed for CAVs operating in arbitrary urban areas. To minimize vehicles' travel times, VeloCity distributes mobility profile optimization directly to individual CAVs. Vehicles query a localized traffic coordinator for a reservation table, independently compute their fastest conflict-free mobility profile, and reserve their requested space-time slots back with the coordinator. By natively adapting to any arbitrary road topology, the framework manages highly irregular urban areas without requiring scenario-specific tuning, all while guaranteeing collision-free and physically executable vehicle trajectories. Extensive simulations across four large-scale real-world urban maps (Tokyo, Manhattan, Rome, and Bologna) demonstrate the framework's scalability. Compared to established state-of-the-art models, VeloCity yields drastically lower travel times, tightly bounds delay variance, and successfully prevents congestion gridlocks even under extremely high vehicular densities.

Problem

• urban traffic congestion
• connected and autonomous vehicles
• multi-agent systems
• scalability
• collision-free trajectory

Innovation

• decentralized multi-agent planning
• spatio-temporal mobility profile
• connected and autonomous vehicles
• collision-free trajectory
• urban traffic scalability