Robust agent-based models for pedestrian dynamics, which can predict the
motion of pedestrians in various situations without specific adjustment of the
model or its parameters, are highly desirable. But the modeller's task is
challenging, in part because it mingles different types of processes (cognitive
and mechanical ones) and different levels of description (global path planning
and local navigation). We argue that the articulations between these processes
or levels are not given sufficient attention in many current modelling
frameworks and that this deficiency hampers the effectiveness of these models.
Conversely, if a decision-making layer and a mechanical one are adequately
distinguished, the former controlling the desired velocity that enters the
latter, and if local navigation is not guided solely by intermediate way-points
towards the target, but by broader spatial information (e.g., a floor field),
then greater robustness can be achieved. This is illustrated with the ANDA
model, recently proposed based on such considerations, which was found to
reproduce a remarkably wide range of crowd scenarios with a single set of
intrinsic parameters.Comment: arXiv admin note: substantial text overlap with arXiv:2309.1279