A Simulation Framework for Synthetic Data Generation and Safety Assessment at Intersections

This study proposes a modelling framework for simulating cyclist–vehicle interactions at urban intersections characterised by geometric constraints and variable visibility conditions. A Digital Model (DM) of the intersection geometry was developed in SUMO, complemented by a custom behavioural model calibrated using experimental trajectory data to capture cyclists’ and drivers’ perception–reaction and braking behaviour. These two components were combined to simulate scenarios with varying visibility conditions and perception-triggered braking responses in severe conflict situations. Results show that reduced visibility significantly reduces temporal safety margins, with over 50% of all simulated interactions yielding differential time-to-arrival (TTA₂) values below 2 s. Furthermore, obstructed conditions lead to higher- and more-dispersed relative crossing speeds (DV), typically increasing by 0.5–1.0 m/s compared to unobstructed conditions. Simulation data confirmed that clear visibility promotes anticipatory and adaptive user behaviour, whereas limited sightlines reduce braking availability and increase the likelihood and severity of conflicts, with distributions conditioned by the intersection’s geometry. The ability to generate detailed synthetic datasets of cyclist–vehicle interactions, often not obtainable through field observation, demonstrates the potential of the proposed framework for safety assessment. This approach supports the evaluation of mitigation strategies, including C-ITS-based solutions, and provides a basis for developing predictive AI models to enhance the safety of vulnerable road users.