Incremental calibration of seat selection preferences in agent-based simulations of public transport scenarios

The calibration of agent-based pedestrian simulation models requires empirical data. To avoid cost-intensive real-world experiments, human-in-the-loop simulations can be applied in which simulated pedestrians interact with human-controlled agents. However, the experiment results may be unrealistic if the human participants are presented with agents acting according to an uncalibrated model. We propose an incremental calibration approach that aims to address the circular dependency between the behaviour of human and simulated pedestrians. By incrementally adapting the parameters of the simulated agents to match the behaviour of the human participants, we aim to gradually approach a realistic interaction. We evaluate our approach using the simulation of the boarding procedure of a public transport vehicle in 2D and virtual reality experiments. The calibration results are compared with those gathered from a traditional non-incremental calibration. Our results indicate the feasibility of our approach and highlight the necessity for future research on efficient simulation model calibration.