Effects of scaling down the population for agent-based traffic simulations

Agent-based transport models simulate the travel demand of individuals at a fine resolution. However, when large road networks and numerous agents are simulated, they require long runtimes. In order to achieve reasonable runtimes, a common strategy is to randomly subsample the entire population. The capacity of the road network is scaled down proportionally. Previous studies have found that scaling affects the simulated agents’ travel time and distance, although the reasons behind that and the size of the impact remain unclear for the scientific community. In this paper, we present a systematic analysis of the consequences of scaling down the synthetic population. With this goal, we simulate a scenario with different scale factors between 1 and 100% using the multi-agent simulator MATSim. Different numbers of model iterations and two road network resolutions are compared. The output of the simulations shows how the scaling process modifies the model runtime, the travel times or the link volumes.