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A simulation model of pedestrian wayfinding behaviour in familiar environments

In this paper, we present a novel approach to simulate wayfinding behaviour of pedestrians familiar with their environment. This approach is inspired from spatial cognition and space syntax domains in order to achieve naturally crowd navigation. Therefore, the proposed wayfinding process is incremental; route choice decisions are made at every street junction, taking into account spatial configuration and individual knowledge of the environment as well as individual preferences. An adequate environment description is provided; it is a graph automatically generated, informed with pre-calculated data, that is used by agents to quantify the benefit cost of a route choice. The environment description is also used to endow agents with mental maps which contain the regions supposed to be experienced by them without going through a learning phase. Obtained results demonstrate that, under our model, agents calculate paths that have the same characteristics as those chosen by pedestrians familiar with their surroundings.