Development of Data-Driven Agent Model for Consumer Shopping Behavior in Commercial Facility

Abstract

The use of ICT is becoming common in architectural design. It is expected that the increasing use of simulations and various AI technologies will lead to further improvements in the evaluation of spatial performance during the design stage. This study develops a data-driven agent model to simulate consumer shopping behavior in a commercial facility, with the ultimate aim of identifying the optimal store arrangement and passage shape. The subject commercial facility is a shopping center with 232 stores and 68,640 square meters of floor space. Four stories of the facility are above ground and one story is below. The data obtained from the facility and used in the model included (1) the number of entering/exiting visitors, (2) the purchase histories of membership cardholders, and (3) the number of visitors passing various points in the facility’s passageways. The membership cards of shoppers gave the authors access to 300,000 to 400,000 purchase histories of approximately 100,000 cardholders per month. To model consumer shopping behavior, the transition probabilities of moving between stores were generated from the available purchase histories. The study’s reproducibility was verified by comparing the simulated results to actual data on the number of remaining visitors, exiting visitors and visitors passing through specific points in the passages.