VeloCity

Tokyo	Manhattan
Rome	Bologna

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

Abstract

As modern cities face increasingly severe traffic congestion, connected and autonomous vehicles (CAVs) have emerged as a crucial enabling factor 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 lock 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.

For the pre-print version of the paper, please visit: http://arxiv.org/abs/2608.08035

If you find this work useful in your research, please consider citing:

@misc{amorosa2026decentralized,
      title={Decentralized Multi-Agent Urban Traffic Management via Spatio-Temporal Mobility Profile Planning}, 
      author={Lorenzo Mario Amorosa and Lorenzo Farina and Vittorio Todisco and Alessandro Bazzi},
      year={2026},
      eprint={2608.08035},
      archivePrefix={arXiv},
      primaryClass={cs.MA},
      url={https://arxiv.org/abs/2608.08035}, 
}