## 6 Conclusions  
This paper introduced and validated a novel methodology for generating synthetic strain-rate signals simulating Distributed Acoustic Sensing (DAS) measurements using outputs from transport microsimulation models (PTV VISSIM). By integrating detailed traffic modeling with physics-based ground deformation simulation (Flamant-Boussinesq), the framework provides a versatile tool for generating large-scale, labeled datasets representing diverse traffic scenarios and sensor configurations. We demonstrated the utility of this approach by training U-Net models exclusively on synthetic data to perform key transport-based DAS signal processing tasks: enhancing spatial resolution, filtering traffic by direction, and simulating alternative fiber-optic cable layouts. Validation on synthetic data confirmed the models' ability to learn these transformations. Application to real DAS measurements from the NA-150 road showed a relevant transfer of capabilities, notably enhancing the visibility of challenging faint signals (bicycles) even in areas with poor sensor coupling. However, the application also highlighted limitations, primarily the domain gap between the current synthetic data (with simplified physics and noise models) and complex real-world signals, leading to noise amplification and imperfect filtering. Key areas for future improvement include developing more realistic noise models based on real data analysis and incorporating calibration procedures to refine model parameters and channel mapping. The inherent assumptions of the physical model (homogeneous medium, uniform coupling) also represent limitations to be addressed in future refinements. Despite these areas for improvement, the proposed framework offers significant potential. It provides a cost-effective and controllable means to generate large labeled datasets for training and evaluating DAS processing algorithms, aids in optimizing sensor deployment strategies by simulating different layouts, and allows exploration of challenging traffic sec narios. This work bridges transport engineering and DAS sensing, contributing a valuable tool for advancing intelligent transportation systems and smart city monitoring initiatives by leveraging the unique capabilities of DAS technology. Continued development, particularly focusing on improved realism through better noise modeling and calibration, will further enhance the methodology's impact. Conceptualization and methodology, I.R.-U. ; software, I.R.-U., J.B., J.B.G. and P.D.G. ; validation, I.R.-U., J.B., J.B.G. and P.D.G. ; formal analysis, I.R.-U. ; investigation, I.R.-U. ; resources, A.C.; data curation, I.R.-U. ; writing--original draft preparation, I.R.-U. ; writing--review and editing, J.B., A.L., M.S., L.R.-C. and A.C.; visualization, I.R.-U. ; supervision, L.R.-C. and A.C.; project administration, A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Government \"Subprograms Aydas Predoctorales 2020 Investigadores\" program under Grant PRE2020-096336, assigned to the National Plan project PID2019-107270 RB-C21, \"Photonic Devices and Systems Sensors for Intelligent Structures and Non Destructive Evaluation I\"; the National \"Knowledge Generation Projects\" project \"Photonic Sensors for Smart and Sustainable Cities (Performance)\" under grant ID6448137269-137269-4-22; the project \"Integrated System for Traffic and Road Condition Monitoring Using Fiber-Optic Sensors (Ingestion)\", under grant PDC2021-121172-C22 funded by MICIU/ AEI/10.13039/501100011033 and by the European Union Next GenerationEU/PRTR). This work was supported in part by grant PID2022-137269 OB-C21 by MICIU/ AEI/10.13039/501100011033 and by ERDF \"A way of making Europe\"; and grant PID2022-137269 OB-C22 by MICIU/ AEI/10.13039/501100011033 and by the European Union. Not applicable.