Researchers have developed a physics-informed neural network framework that turns standard fiber-optic cables commonly used for communication into large-scale traffic sensors. By embedding traffic flow equations into deep learning models, the team achieved accurate traffic speed and flow estimation across long-distance expressway using existing telecom infrastructure without installing new hardware.
The team published their study in Communications in Transportation Research ( https://doi.org/10.26599/COMMTR.2026.9640043 ).
Distributed acoustic sensing (DAS) turns standard fiber‑optic telecommunication cables, already buried along most highways, into dense vibration sensors that can monitor traffic in real time. But raw DAS signals are noisy and indirect: overlapping vehicle signatures often produce coarse, fragmented traffic maps that lack the precision needed for large‑scale traffic management.
To solve this, researchers at Hefei University of Technology, USTC, and Tsinghua University developed a physics‑informed neural network (PINN) framework that restores accurate traffic flow and speed maps from DAS data.
“We treat DAS‑based traffic state perception as an image restoration problem,” says Yang Ma, Associate Professor at Hefei University of Technology. “The key is embedding the fundamental laws of traffic flow, such as the Lighthill‑Whitham‑Richards conservation equation, directly into the neural network’s loss function. This ensures predictions are physically consistent, not just statistically plausible.”
The team built a realistic DAS simulation platform, validated with field data from China’s Chuxin Expressway, and tested 16 model combinations. The LWR‑constrained models outperformed purely data‑driven baselines by over 30% in error reduction, while more complex physics models actually degraded performance due to noise amplification. They also found that fusing DAS with camera data can help, but denser camera placement does not always improve accuracy, it can introduce spatial discontinuities that destabilize the physics loss.
Crucially, few‑shot transfer learning allowed the pre‑trained model to adapt to new highways (e.g., US‑101 and I‑80) with only a few hundred local samples, restoring high accuracy despite major differences in road geometry and traffic patterns.
“This means we can deploy a generalized model across diverse expressways with minimal site‑specific calibration,” Ma explains. “Instead of installing new hardware, we repurpose existing fibers as a distributed sensing network, which is scalable, cost‑effective, and can work in all‑weather conditions.”
The approach offers a paradigm shift from capital‑intensive sensor installation to infrastructure‑as‑sensor, paving the way for truly ubiquitous traffic monitoring on long‑distance highways.
DOI Link:
https://doi.org/10.26599/COMMTR.2026.9640043
About Communications in Transportation Research
Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University, and Professor Xiaopeng (Shaw) Li from University of Wisconsin–Madison. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.
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Communications in Transportation Research
Leveraging distributed acoustic sensing for large-scale expressway traffic state perception
30-Sep-2026