Fast-forwarding agent states to accelerate microscopic trafic simulations

Traditionally, the model time in agent-based simulations is advanced in ixed time steps. However, a purely time-stepped execution is ineicient in situations where the states of individual agents are independent of other agents and thus easily predictable far into the simulated future. In this work, we propose a method to accelerate microscopic traic simulations based on identifying independence among agent state updates. Instead of iteratively updating an agent’s state throughout a sequence of time steps, a computationally inexpensive łfast-forwardž function advances the agent’s state to the time of its earliest possible interaction with other agents. To demonstrate the approach in practice, we present an algorithm to eiciently determine intervals of independence in microscopic traic simulations and derive a fast-forward function for the popular Intelligent Driver Model (IDM). In contrast to existing acceleration approaches based on reducing the level of model detail, our approach retains the microscopic nature of the simulation. A performance evaluation is performed in a synthetic scenario and on the road network of the city of Singapore. At low traic densities, we achieved a speedup of up to 2.8, whereas at the highest considered densities, only few opportunities for fast-forwarding could be identiied. The algorithm parameters can be tuned to control the overhead of the approach.