Leveraging distributed acoustic sensing for large-scale expressway traffic state perception
This paper introduces distributed acoustic sensing (DAS) as an emerging sensing technique for large-scale traffic state perception (TSP) on expressways. By enabling optical fibers to operate as dense sensing arrays, DAS offers a promising solution for continuous traffic monitoring along expressway corridors. However, the raw traffic information extracted from DAS is inaccurate, which limits its direct application to large-scale TSP. This study proposes a physics-informed neural network (PINN) framework for DAS-based TSP. A physics-based DAS simulation platform is developed and validated against field observations which provides a flexible testbed for generating data under diverse traffic scenarios. On this basis, two network architectures, ResUNet and Fourier Neural Operator, are investigated in combination with three types of traffic flow constraints, namely the Lighthill-Whitham-Richards (LWR) model, LWR with a fundamental diagram, and the Aw-Rascle-Zhang model. Totally, 16 PINN models are constructed and evaluated. The results show that the proposed PINN framework effectively improves the accuracy of DAS-based TSP compared to the solely data-driven baselines. Among the physical constraints considered, the LWR-based models show the best overall performance. In addition, few-shot transfer learning can enhance model performance in previously unseen scenarios, demonstrating the potential of the proposed framework for adaptation to site-specific deployment conditions.