Leveraging distributed acoustic sensing for large-scale expressway traffic state perception

This replication package comprises two complementary components: (1) a MATLAB‑based DAS simulation framework that generates synthetic acoustic signals and spatiotemporal traffic maps from microscopic vehicle trajectories, and (2) a Python implementation of physics‑informed neural networks (PINNs) for traffic state restoration, covering 16 model variants with different architectures (ResUNet and FNO) and physical constraints (LWR, LWR+FD, and ARZ). The package includes simulation input files (trajectory data and SUMO road network), a tutorial video for the simulation workflow, pre‑trained weights for two‑channel inputs, and sample training data. The full dataset and additional three‑channel model weights are available via a separate download link.