Calibrating Wayfinding Decisions in Pedestrian Simulation Models: The Entropy Map

This paper presents entropy maps, an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. In particular, entropy maps show the instantaneous level of randomness in decisions of a pedestrian agent situated in a specific point of the simulated environment with an heatmap approach. Experimental results highlighting the relevance of this tool supporting modelers are provided and discussed. Keywords: Data Visualization · Modelling and Simulation · Stochastic Models. 1 Introduction & Related Works Computer simulation of complex systems often employs stochastic models: implied randomness is a way to account for aspects that are potentially relevant to the overall phenomenon but cannot be explicitly considered to keep the model and the modelling phase manageable [3]. Pedestrian and crowd behaviour simulation, for instance, requires considering different kinds of decisions, taken at distinct levels of abstraction, employing heterogeneous information and knowledge about the environment, from path planning [7] to the regulation of distance from other pedestrians and obstacles present in the environment[2,8]. Exploring implications of randomness and situations of indecision, irresolution in case of choice among alternative lines of behaviour such as the exits from an environment in an emergency situation [10], can be a very significant step, with important implications of overall simulation results. This paper presents an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. As in the framework of probability theory [12], we use the concept of entropy to provide a measure of uncertainty over the simulated space The paper, first of all, describes a general decision making model for supporting wayfinding, which comes from previous work by the authors [8,7].