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.
By Hao Tian, Heng Cai, Xiaowei Chen, Yifan Yang
SeisBench DAS is an extension of the SeisBench library that standardizes distributed acoustic sensing (DAS) data, metadata, labels, and models for machine learning. It leverages the xdas framework for data ingestion and virtual array handling, and PyTorch for model application, providing an efficient engine to apply deep learning models across diverse DAS formats. The framework aims to bridge the gap between model developers and practitioners, facilitating the adoption of deep learning in DAS research and allowing easy integration of future developments.
By Jannes M\"unchmeyer, Han Xiao, Frederik Tilmann
arXiv:2607. 24056v1 Announce Type: cross Abstract: Network-wide traffic volume estimation typically relies on propagating measurements from fixed sensors, making performance highly dependent on sensor density and limiting deployment in sparsely instrumented networks.
By L\'eo Hein, Giovanni De Nunzio, Aur\'elie Pirayre, Laurent Najman
The paper presents a framework that adapts general-purpose Vision Foundation Models (VFMs) to seismic data denoising using Parameter‑Efficient Fine‑Tuning with Low‑Rank Adaptation (LoRA). It introduces a kurtosis‑guided unsupervised test‑time adaptation module that updates only LoRA parameters to self‑calibrate for site‑specific noise without ground truth. Experiments on exploration seismic images and DAS data demonstrate that the approach matches or surpasses domain‑specific models and generalizes well to unseen cross‑site data.
By Jiahua Zhao, Umair bin Waheed, Jing Sun, Yang Cui, Nikos Savva, Eric Verschuur
arXiv:2606. 00081v1 Announce Type: cross Abstract: Distributed Acoustic Sensing (DAS) enables large-scale monitoring through optical fibers, but its high dimensionality and complex spatio-temporal patterns make event classification demanding.
By Michel Dione (CERI SN - IMT Nord Europe), Jerry Lonlac (CERI SN - IMT Nord Europe), H\'el\`ene Louis (CERI SN - IMT Nord Europe), Anthony Fleury (CERI SN - IMT Nord Europe), Stephane Lecoeuche
arXiv:2606. 15642v1 Announce Type: cross Abstract: Accurate traffic flow prediction remains challenging in cross-city, data-scarce scenarios where limited historical data hinders model generalisation.
By Abdul Joseph Fofanah, Lian Wen, David Chen, Shaoyang Zhang
arXiv:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.
By Yongchao Li, Yang Li, Zhuoxuan Li, Jun Chen, Chu Zhang, Jinde Cao, Leszek Rutkowski
arXiv:2609.13878v1 Announce Type: new
Abstract: Spatio-temporal traffic data are central to intelligent transportation systems, yet their heterogeneity poses significant challenges for large-scale mo...
By Zhouyang Liu, Jindong Han, Hao Wang, Xinyue Liu, Hui Gao, Dongsheng Li, Hao Liu
arXiv:2510. 03381v3 Announce Type: replace-cross Abstract: Interchanges are crucial nodes for vehicle transfers between highways, yet the lack of real-time ramp detectors creates blind spots in traffic prediction.
By Yongchao Li, Jun Chen, Zhuoxuan Li, Chao Gao, Yang Li, Chu Zhang, Changyin Dong
arXiv:2608. 13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations.
By Davide Andrea Guastella, Eladio Montero Porras, Evangelos Pournaras, Gianluca Bontempi
The paper introduces the Spatio-temporal Long-term Partial sensing Forecast model (SLPF) for predicting long-term traffic when sensors are only available at some locations. It tackles challenges such as unknown data distribution at unsensed sites, complex spatio-temporal correlations, and noise by employing a rank-based embedding, a spatial transfer matrix, and a multi-step training process. Experiments on real-world datasets show that SLPF outperforms existing methods.
By Zibo Liu, Zhe Jiang, Zelin Xu, Tingsong Xiao, Zhengkun Xiao, Yupu zhang, Haibo Wang, Shigang Chen
arXiv:2602.14049v2 Announce Type: replace-cross
Abstract: Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as si...
By Yue Wang, Areg Karapetyan, Djellel Difallah, Samer Madanat