Fast-Forwarding of Vehicle Clusters in Microscopic Traffic Simulations

State fast-forwarding has been proposed as a method to reduce the computational cost of microscopic traffic simulations while retaining per-vehicle trajectories. However, since fast-forwarding relies on vehicles isolated on the road, its benefits extend only to situations of sparse traffic. In this paper, we propose fast-forwarding of vehicle clusters by training artificial neural networks to capture the interactions between vehicles across multiple simulation time steps. We explore various configurations of neural networks in light of the trade-off between accuracy and performance. Measurements in road network simulations demonstrate that cluster fast-forwarding can substantially outperform both time-driven state updates and single-vehicle fast-forwarding, while introducing only a small deviation in travel times.