Abstract
Agent-based modeling is a computational methodology that allows the analyst to create, analyze, and experiment with artificial worlds populated by agents. A specific research area is microscale agent-based modeling, which can be used for the simulation of pedestrian movement for low- and high-density scenarios and for the effect of changes in an environment. Such models can also be used for pedestrian dynamics in city centers to show the design effects in the shopping environment. The main contribution of this article is an agent-based model that provides an activity agenda for pedestrian agents that guides their shopping behavior in terms of destination and time spent in shopping areas. This model involves choice mechanisms including where to stop, in what order, and which route to take. The article describes a framework for processing agent-based pedestrian activity simulations within a shopping environment. The main achievement of this research is a validation of the approach leading to a working system. Preliminary findings are reported here.
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