When deployed along transport networks, these systems sense ground deformations induced by passing vehicles (Figure 1), enabling long-range traffic monitoring with unique advantages in spatial coverage compared to traditional sensors. While DAS has demonstrated value in geophysics and infrastructure monitoring [1; 2; 3], its application to traffic analysis still faces several challenges. The key challenges include  
Figure 1: Synoptic overview of real roadside DAS signal example from opposing traffic: three westbound lightweight vehicles (bicycles) crossing three heavy eastbound vehicles. (**a**) Vehicle weight causes quasi-static ground deformation (cyan), while tire–road interactions induce dynamic deformation (red). A buried fiber-optic cable (yellow) senses both. Black dots indicate approximate gauge length; (**b**) the quasi-static signals from the heavy vehicles produce trackable patterns over the DAS channels, while lighter vehicles are less distinct; (**c**) dynamic deformation is more complex, though its amplitude can be higher; (**d**) strain rate recording at one single DAS channel (white dotted line in (**b**,**c**). achieving sufficient signal-to-noise ratio (SNR) and spatial resolution, especially in dense traffic, where heavier vehicles can mask lighter ones [4]. Existing methods using wavelet thresholds [5] and template matching [6] struggle in dense traffic scenarios and bidirectional flows, where signal overlaps from multiple vehicles degrade performance. Recent machine learning approaches [7] show promise but remain constrained by limited manually labeled datasets and the inability to model non-ideal cable layouts common in urban deployments. Often, DAS deployment relies on existing \"dark fiber\" cable infrastructure near roads [8; 9]. These pre-existing cable layouts may not be optimal for specific traffic monitoring tasks due to placement or distance variations. While potentially cost-effective for large networks [10], optimizing cable layout design can significantly improve detection [9]. Generating realistic synthetic DAS datasets is crucial not only for evaluating the benefit of potential custom cable layouts but also for developing robust signal processing algorithms before any field deployment. Furthermore, most current techniques rely solely on point-specific real-world data collection [11], even if assisted with cameras [12], reducing their capacity to capture sufficiently diverse traffic scenarios and sensor configurations to generalize the analysis for long networks. Not having a balanced and sufficiently high number of examples for each type of vehicle, traffic condition, and cable/transport network combination can reduce the applicability of many processing techniques. The customization of the processing techniques relies on expensive data acquisition surveys, unless adequate synthetic data are produced. Synthetic DAS analysis already exists in the seismology [8; 13; 14] but has only been used in specific traffic monitoring cases, such as [15], where synthetic labeled datasets were used to train machine learning (ML) models. By integrating synthetic dataset generation with transport models, we can create an unlimited supply of labeled data to support traffic agnostic data generalization for any specific network, regardless of the fine-tuning process that could be applied afterwards. Our work addresses these limitations through a novel synthesis of transport microsimulation and physics-based modeling. Unlike prior DAS traffic studies that either use simplified vehicle models [9] or focus on post hoc analysis of field data [16], we integrate detailed vehicle trajectory simulations from PTV VISSIM with strain modeling via the Flamant-Boussinesq approximation (see Appendix A). This integration enables the generation of scenario-controlled synthetic datasets that are stochastically realistic and are customized for a given network.