An agent-based model for plausible wayfinding in pedestrian simulation

Modeling pedestrian decision making activities represents a serious challenge: different decisions are taken at distinct levels of abstraction, employing heterogeneous information and knowledge about the environment, from path planning to the regulation of distance from other pedestrians and obstacles present in the environment. Pedestrians, moreover, are not robots: although empirical observations show that they consider congestion when planning, there are also evidences that their decisions are not always optimal, even in normal situations. We present a model integrating and improving consolidated results mitigating the optimization effects of congestion aware path planning. In particular, we employ commonsense estimations of the effects of perceivable congestion instead of exact values, also embedding an imitation mechanism stimulating changes in planned decisions whenever another nearby pedestrian did the same. The model leads to improvements in quantitatively reproducing observed phenomena, both in a validation scenario as well as in a real-world situation: an interesting counterintuitive result, in which reducing available choices and exits actually reduces overall egress time, is also presented and discussed.