以高时空分辨率映射交通动态分布是交通运输研究中的基础问题。分布式声学传感（DAS）作为一种创新的 seismic observation 工具，成为在高空间和时间尺度上进行实时城市交通监测的有前景解决方案。DAS 将现有的地下光纤电缆重新用作密集、连续的传感器阵列，从而能够以米级空间分辨率和秒级时间分辨率对道路上的交通活动进行被动且保护隐私的监测。本研究探讨了整合 DAS 与深度学习模型能否作为连续高效的城市交通观测站，以揭示高时空分辨率下的城市交通动态（即交通流量、拥堵及事件驱动的变化）。基于在美国德克萨斯州大学城（College Station）沿路网部署的 DAS 系统，本研究开发了一个由深度学习赋能的分析框架，将原始地面振动波形转换为时空表示，检测车辆轨迹，并从聚合的交通流量和速度中推断交通状态。采用结合合成数据与人工标注 DAS 图像的混合训练策略，以提高噪声和拥堵条件下的车辆检测性能，并将模型输出进一步聚合以表征系统级交通动态。

Mapping the distribution of traffic dynamics at high spatiotemporal resolution is a fundamental question in transportation research. Distributed acoustic sensing (DAS), an innovative seismic observation tool, emerges as a promising solution for real-time urban traffic monitoring at high spatial and temporal scales. Distributed acoustic sensing repurposes existing underground fiber-optic cables as dense, continuous sensor arrays, enabling passive and privacy-preserving monitoring of roadway traffic activity at meter-level spatial and second-level temporal resolution. This study examines whether integrating DAS and deep learning models can serve as a continuous and efficient urban traffic observatory for revealing urban traffic dynamics (i.e. traffic volume and congestion, event-driven changes) at high spatiotemporal resolution. Using a DAS deployment along a roadway network in the City of College Station, Texas, USA, this study develops a deep learning-empowered analytical framework that converts raw ground vibration waveforms into spatiotemporal representations, detects vehicle trajectory, and infers traffic states from aggregated traffic volume and speed. A hybrid training strategy combining synthetic and manually annotated DAS images is used to improve vehicle detection under noisy and congested conditions, with model outputs further aggregated to characterize system-level traffic dynamics.