Keywords: Agent-Based Modelling; Simulation Systems; Retail Dynamics; Retail Real Estate Development; Adaptive Retail Reuse; Shoppers’ Behaviour; Retailers’ Behaviour
This study presents an agent-based pilot simulation system to examine the interactions between
customers’ and retailers’ behaviour in Dutch downtown retail areas. With the rise of e-commerce and
the growing number of vacant stores, this situation is putting pressure on downtown retail. The research
is guided by the central research question: “How can downtown retail dynamics be simulated?” The
goal of the study is to lay the foundation of an advanced predictive simulation tool to aid the
development of strategies that revitalize downtown retail areas. Based on an extensive literature review,
the study identifies the various factors that shape retailing activity. By simulating various scenarios in
the system, each emphasizing different influential factors, it was concluded that the majority of findings
were successfully replicated by the simulation. Higher RFA weights emphasized the significance of
store size. Distance from customer to store, even though the spatial areas was rather limited in the
system, did affect customer preference. Effects of clusters of stores and anchor store proximity were
less clear. These were implemented in the system by using the MNL model with an utility function in
combination with a Monte Carlo simulation. Impulse purchases made the retail area flourish. Retailers
were seen to be constantly trying to optimize their profitability through positioning in high foot traffic
locations, and near anchor stores. Retailers were not observed clustering with similar retailers. The
retailers however also have to make sufficient amount of sales in order to afford rent, leading to dynamic
adaption such as vacancies or transformations when profit is not sustainable. Hinting the retail dynamics
of the simulation system. Further, spatial factors like entry points also influence customer movement.
Furthermore, the parameters used in the simulation, such as the weights of the utility function and rates
of transformation are based on assumptions. Further research should validate and refine these parameters
through empirical studies at greater depth. The prediction power could be enhanced if customers’
behaviour models are improved, dynamics affecting retailers such as rent changes are being incorporated
and GIS-based spatial data are being integrated into the system. With these advances, the simulation
could become an effective and adaptable tool for urban planning and policy-making.