Traffic digital twins, which inform policymakers of effective interventions based on large-scale, high-fidelity computational models calibrated to real-world traffic, hold promise for addressing societal challenges in our rapidly urbanizing world. However, conventional fine-grained traffic simulations are non-differentiable and typically rely on inefficient gradient-free optimization, making calibration for real-world applications computationally infeasible. Here we present a differentiable agent-based traffic simulator that enables ultra-fast model calibration, traffic nowcasting, and control on large-scale networks. We develop several differentiable computing techniques for simulating individual vehicle movements, including stochastic decision-making and inter-agent interactions, while ensuring that entire simulation trajectories remain end-to-end differentiable for efficient gradient-based optimization. On the large-scale Chicago road network, with over 10,000 calibration parameters, our model simulates more than one million vehicles at 173 times real-time speed. This ultra-fast simulation, together with efficient gradient-based optimization, enables us to complete model calibration using the previous 30 minutes of traffic data in 455 s, provide a one-hour-ahead traffic nowcast in 21 s, and solve the resulting traffic control problem in 728 s. This yields a full calibration--nowcast--control loop in under 20 minutes, leaving about 40 minutes of lead time for implementing interventions. Our work thus provides a practical computational basis for realizing traffic digital twins.

翻译：交通数字孪生技术通过构建与真实世界校准的大规模高保真计算模型，为政策制定者提供有效干预措施，有望解决快速城市化进程中的社会挑战。然而，传统细粒度交通仿真模型不可微分，通常依赖低效的无梯度优化方法，使得面向实际应用的模型校准在计算上不可行。本文提出一种可微分智能体交通仿真器，能够在大规模路网上实现超快模型校准、交通实时推演与控制。我们开发了多项可微分计算技术用于模拟单个车辆运动，包括随机决策过程与智能体间交互，同时确保整个仿真轨迹保持端到端可微分特性以支持高效梯度优化。在包含超过10,000个校准参数的大规模芝加哥道路网络中，本模型以173倍实时速度仿真超过一百万辆车辆。这种超快仿真能力与高效梯度优化相结合，使我们能够在455秒内利用前30分钟交通数据完成模型校准，21秒内完成未来一小时交通实时推演，并在728秒内求解相应交通控制问题。由此形成的完整校准-推演-控制循环耗时不足20分钟，为实施干预措施预留约40分钟时间裕度。本研究为构建交通数字孪生系统提供了切实可行的计算基础。