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arXiv Machine Learning By Hao Tian, Heng Cai, Xiaowei Chen, Yifan Yang

Monitoring Urban Traffic Dynamics at Fine Spatiotemporal Resolution Using Distributed Acoustic Sensing and Deep Learning

Read the original on arXiv Machine Learning →

The paper presents a method that uses distributed acoustic sensing (DAS) and deep learning to monitor urban traffic at fine spatiotemporal resolution. By repurposing underground fiber‑optic cables as dense sensor arrays, the approach captures roadway activity at meter‑level spatial and second‑level temporal scales. A deep learning framework processes raw vibration waveforms to detect vehicle trajectories and infer traffic volume and speed, with a hybrid training strategy that combines synthetic and manually annotated data to improve detection in noisy, congested conditions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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