## 4 Results
### Performance on Synthetic Data (Validation)  
The U-Net models were trained and validated using the synthetic datasets generated as described in Sections 2 and 3. The objective was to quantitatively and qualitatively assess their ability to perform the three target tasks (resolution enhancement, directional filtering, layout simulation) within the controlled synthetic domain. * **Quantitative Evaluation:*
* Validation performance was measured using mean squared error (MSE) and mean absolute error (MAE) between the model outputs and the target synthetic \"label\" datasets. The key results for models trained on the combined traffic density datasets with added input noise (std = 0.005) are summarized in Table 1 (from Appendix B). * **Westbound (WB) Filtering and Resolution Enhancement:*
* Achieved the lowest error (validation **MSE = 0.0015**, MAE = 0.0119), indicating high fidelity in isolating WB traffic and upscaling resolution while preserving the original layout's characteristics. * **Eastbound (EB) Filtering, Resolution Enhancement, and Alternative Layout:*
* Showed significantly higher error (**MSE = 0.1899**, MAE = 0.0460). This reflects the increased complexity of simultaneously filtering EB traffic and transforming the signal to simulate a different cable path (straight EB shoulder). * **Middle-of-Lane (MID) Layout Simulation and Resolution Enhancement:*
* Also had considerable error (**MSE = 0.1350**, MAE = 0.0521), indicating the challenge of transforming signals from the WB shoulder layout (with diversions) to simulate a mid-road layout while processing traffic from both directions and balancing amplitudes. * **Observed Improvements:*
* Visual inspection of the processed synthetic data confirms the intended effects (See Figure 6). * _Spatial Resolution Enhancement_: All models successfully doubled the spatial resolution from 1.034 m (960 ch, Figure 6b) to 0.517 m (1920 ch, Figure 6d,f,h). This results in visually sharper and more distinct vehicle traces, potentially improving downstream tasks like vehicle counting or headway estimation. * _Directional Filtering:_ The WB model effectively isolated westbound traffic (Figure 6d), retaining the signal degradation effects in Strips A and B charac
teristic of the original layout. The EB model isolated eastbound traffic (Figure 6f). The separation appears clean in the synthetic domain. * _Layout Transformation:_ The EB model (Figure 6f) successfully compensated for the signal loss/distortion in Strips A and B, producing a signal consistent with the target straight EB shoulder layout (compare with Figure 6e). The MID model (Figure 6h) similarly compensated for Strips A and B and attempted to balance signal amplitudes between WB and EB traffic, consistent with the target mid-road layout (Figure 6g). Figure 6: Synthetic DAS experiments. Column 1 (a,c,e,g) shows cable distance correspond to the DAS channels vs. the lateral separation of the cable from road centerline, for different cable layouts:  
(**a,c**) original WB shoulder layout, (**e**) target EB shoulder layout, and (**g**) target MID-road layout. Column 2 (**b,d,f,h**) shows corresponding synthetic strain-rate signals (waterfall plots). (**b**) Input signal (960 ch) showing all traffic and effects of Strips A and B (with higher cable lateral separation due to the Abandoned Service Station and the Urbi River Bridge, respectively). (**d,f,h**) Processed outputs (1920 ch) with enhanced resolution: (**d**) WB-filtered result (original layout), (**f**) EB-filtered result (simulating EB layout, note absence of Strip A/B effects), and (**h**) all-traffic result (simulating MID layout, compensated for A/B). Sharpness of traces is compared in (**d,f,h**) vs. (**b**). * **Interpretation:** The quantitative metrics validate U-Net's ability to learn the desired transformations from synthetic data. The low MSE for the WB task demonstrates high accuracy when the target preserves the original layout geometry.