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Robust agent-based models for pedestrian dynamics, which can predict themotion of pedestrians in various situations without specific adjustment of themodel or its parameters, are highly desirable. But the modeller's task ischallenging, in part because it mingles different types of processes (cognitiveand mechanical ones) and different levels of description (global path planningand local navigation). We argue that the articulations between these processesor levels are not given sufficient attention in many current modellingframeworks and that this deficiency hampers the effectiveness of these models.Conversely, if a decision-making layer and a mechanical one are adequatelydistinguished, the former controlling the desired velocity that enters thelatter, and if local navigation is not guided solely by intermediate way-pointstowards the target, but by broader spatial information (e.g., a floor field),then greater robustness can be achieved. This is illustrated with the ANDAmodel, recently proposed based on such considerations, which was found toreproduce a remarkably wide range of crowd scenarios with a single set ofintrinsic parameters.

Revisiting the theoretical basis of agent-based models for pedestrian dynamics