随着现代城市交通拥堵日益加剧，网联自动驾驶车辆（CAVs）已成为下一代智能交通管理的关键使能技术。然而，当前范式存在若干局限，制约了该潜力的充分实现：现有方法通常仅优化局部交互而非系统级效率，通信开销巨大，或缺乏运动学安全执行所必需的确定性保障；此外，当前多智能体方法常局限于小规模预定义场景，难以扩展至大规模复杂城市路网。为弥合这一差距，本文提出 VeloCity——一种面向任意城市区域中 CAV 运行的去中心化多智能体时空移动轮廓规划框架。为最小化车辆行程时间，VeloCity 将移动轮廓优化任务直接下放至各 CAV 个体：车辆向本地交通协调器查询预约表，独立计算其最快且无冲突的移动轮廓，并将所需时空槽位请求反馈给协调器完成预约。该框架原生适配任意道路拓扑结构，无需针对特定场景调参即可管理高度不规则的城市区域，同时严格保障车辆轨迹无碰撞且物理可执行。在东京、曼哈顿、罗马和博洛尼亚四张大规模真实城市地图上的大量仿真表明，该框架具备良好可扩展性；相较于现有先进模型，VeloCity 显著降低行程时间，严格约束延迟方差，并在极高车流密度下仍可有效避免拥堵死锁。

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.