FLAME-GPU for traffic systems: a scalable agent-based simulation framework

Agent-based modelling (ABM) has revolutionised the simulation of complex systems, finding applications in diverse fields such as economic markets and traffic management. By modelling individuals as autonomous agents within a dynamic environment, ABM enables the exploration of system behaviours and the evaluation of interventions at various spatiotemporal resolutions. However, the computational intensity of ABM, particularly in large-scale simulations, remains a significant hurdle. This paper presents a novel approach to addressing these challenges through the development of a GPU-accelerated transport model, specifically applied to a road network. Utilising the FLAME-GPU framework, the proposed model demonstrates enhanced scalability and efficiency compared with traditional CPU-based simulations, such as Simulation of Urban MObility (SUMO). Through rigorous comparative analysis, this study highlights significant improvements in simulation speed and the capacity to manage larger vehicle populations. The research underscores the transformative potential of GPU acceleration in mitigating computational constraints within ABM, offering a practical framework for simulating transport systems with greater precision and depth. Extensive experimentation validates the model’s ability to realistically simulate the vehicle population of the Isle of Wight, achieving a balance between computational efficiency and the accurate representation of complex traffic dynamics.