Abstract
Shop-around spatial behaviors of downtown visitors are characterized as MultiPurpose-MultiStop (MPMS). However, the authors’ investigations have revealed visitors frequently switch planned actions and generate improvised actions. By using an agent-based approach, especially with a medium-size specimen, simulating such spatial behaviors opens a rich vein of research, not only into such practical aspects as downtown revitalization but also several theoretical aspects. Based on data analysis, the authors have newly devised Agent Simulation of Shop-Around (ASSA). ASSA is a kind of activity-based model and each agent makes and remakes their schedule to visit shops based on time constraints and shop preferences, chooses alternative venues to visit when they fail in an errand, and makes impulse stops at shops and detour actions when time allows. A series of such activities carried out on one day will affect the next downtown visit schedule and so on. This paper refers to existing researches and briefly explains the features of ASSA, especially focusing on decomposition of the shop-around behaviors and the system components. The latest pilot ASSA ver.3 attempts a dynamic simulation of naturalistic and intelligent shopper behaviors. The authors then discuss the verifications by illustrating simulated performances in an actual shopping mall.
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