Microscopic traffic simulation represents one of the most popular tools for analysing and comparing traffic performance in different scenarios of urban mobility. The reproduction of real-world traffic dynamics is its primary requirement for providing time-dependent estimates. This study presents a process to build a microscopic traffic model for an urban area of Athens, developed using high-resolution vehicle trajectories obtained from the pNEUMA dataset. Based on drone-recorded trajectories, a simulation scenario was built, combining a realistic road network model, including traffic light regulation, an estimated traffic demand, and a set of parameters to replicate the observed vehicle behaviour. The modelling process relies on an iterative comparison between simulated outputs and observed vehicle-level trajectory data. The proposed approach evaluates travel time distributions and helps develop an improved model, enhancing its ability to replicate the vehicle’s behaviour. The final calibrated configuration reduced the Wasserstein distance by approximately 55.8% compared with the default SUMO configuration. The results also show that the calibration of microscopic behavioural parameters can substantially affect secondary simulation outputs, including emission estimates relevant for sustainability-oriented traffic analyses.

Traffic simulation models are widely used in transportation analysis, often oriented toward keeping urban systems sustainable from various points of view, ranging from energy consumption to air quality. However, their accuracy depends on the quality of the data used to represent both the road network and travel demand. Although open-source datasets can be used to develop simulation networks and observed traffic information is available from big-data platforms, integrating these heterogeneous datasets remains challenging. Indeed, different road segmentation schemes may be used across different platforms, and common identifiers are often not adopted. This study proposes a GIS-based framework for spatially joining traffic data from big-data platforms with road networks used in traffic simulation models. The methodology integrates a microscopic simulation network derived from OpenStreetMap and implemented in SUMO with traffic data obtained from the TomTom Traffic Stats service. The workflow is implemented in QGIS (3.34 prizren) and combines spatial buffering, directional filtering, overlap analysis, and hierarchical match cleaning to associate traffic segments with the corresponding simulation network edges. The framework is applied to an urban case study in the city of Biella, Italy. Results show that more than 80% of the simulation network edges can be successfully linked with traffic segments, enabling the integration of hourly traffic indicators such as travel time and speed. The resulting dataset supports several applications, including network calibration, simulation validation, detector placement, and traffic demand estimation, contributing to the development of more reliable traffic simulation models for comparing and selecting sustainable urban mobility actions within the transportation planning process.