利用分布式声学传感与深度学习实现细粒度时空分辨率的城市交通动态监测
Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning
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中文总结 AI 辅助
本研究利用分布式声学传感和深度学习，构建了高时空分辨率的城市交通监测框架，实现了车辆轨迹检测与交通状态推断，为实时交通动态观测提供了高效方案。
中文摘要 AI 辅助
以高时空分辨率绘制交通动态分布是交通研究中的一个基本问题。分布式声学传感（DAS）作为一种创新的地震观测工具，成为实现高时空尺度实时城市交通监测的一种有前景的解决方案。分布式声学传感将现有的地下光纤电缆重新用作密集、连续的传感器阵列，从而能够在米级空间分辨率和秒级时间分辨率下，对道路上的交通活动进行被动且保护隐私的监测。本研究探讨了将DAS与深度学习模型相结合，是否能够作为一个连续且高效的城市交通观测站，以高时空分辨率揭示城市交通动态（即交通流量与拥堵、事件驱动变化）。本研究利用美国德克萨斯州大学城（College Station）道路网络上的DAS部署，开发了一个深度学习赋能的解析框架，该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。采用一种结合合成图像和人工标注DAS图像的混合训练策略，以提高在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。
英文摘要
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.
发表机构
- Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。
