Simulating Shop-Around Behavior

Simulating Shop-Around Behavior

Toshiyuki Kaneda (Nagoya Institute of Technology, Japan) and Takumi Yoshida (Bureau of Housing and Planning, Nagoya City Office, Japan)
Copyright: © 2012 |Pages: 14
DOI: 10.4018/jats.2012070102
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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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The shop-around behavior model is also known as the MultiPurpose-MultiStop (MPMS) model and since the 1980s it has been developed and studied in such fields as geography and urban planning, not only for its practical application, e.g., downtown revitalization and town center management, but also for its theoretical interest in the field of spatial analysis (Kelly, 1981). By the 1990s, the application of the logit model that combines data-fitting and approximate utility-maximization, helped establish the ’Markov-chain type’ models that make up transition probability OD-matrices. A typical microsimulation is Linked Logit and Poisson Model (LLPM), with a Poisson assumption on visitor arrival times. However, in the era of agent modeling, limitations pointed out concerning the Markov property, which ignores the personal history of downtown visitors, led to new approaches being explored.

The Logit model can be interpreted as used in an approximate estimation of the random utility, thus LLPM is considered to be a rational model. In other hands, the agent model would be considered as a bounded rational model, so there are at least two types of bounded rational models. One type is a rule-based approach that employs heuristics, which can be interpreted as an expression of ‘procedural rationality,’ as referred to by H. A. Simon. Implementation technologies such as the production system in knowledge engineering and advanced researches had already been made into this approach.

The other type is the assumption-relaxation approach, which relaxes the assumption of perfect rationality with perfect information by adopting the concepts of satisficing or the constraint satisfaction principle. This approach is also based on the ‘satisficing principle’ of mathematical models proposed by Simon and followers (e.g., Rubinstein, 1998).

In agent modeling research the daily activity-travel model has taken the lead in such fields as transportation planning (Table 1). Albatross (Arentze et al., 2001) is formulated as a rule-based system that guarantees data-fitting by employing a data-mining tool to generate heuristic rules (binary tree). Aurora (Arentze, Pelizaro, & Timmermans, 2005) is formulated as a utility-based theoretical model that generates a schedule by combining each activity (errand) that has non-linear S-shape utility and employing genetic algorithms; in addition, in response to an unexpected event such as congestion, the model carries out re-scheduling.

Table 1.
Existing agent modeling researches
AlbatrossAuroraLogit Model Approach (ex. LLPM: Linked Logit Poisson Model, Transition Matrix Models)Kurose's
ASSA ver.3
Deals withDaily Activity-TravelDaily Activity-TravelDaily Activity-Travel / MultiPurpose-MultiStop in Shopping DistrictMultiPurpose-MultiStop in Shopping DistrictMultiPurpose-MultiStop in Shopping District
Principle of ModelingIdea of The SystemHeuristic-Rule BasedAttempts to Keep a Utility- Maximized Schedule under Constraints/EventsUtility-Maximization BasedHeuristic-Rule BasedUtility/Constraints- Satisfaction Based
Model TypeBounded RationalBounded Rational but Adaptation/Intelligent FunctionsRationalBounded RationalBounded Rational but Adaptation/Intelligent Functions
/ Intelligent
Schedule Planning
YES but Conditional
Rule Expression
YESNOYES but Conditional
Rule Expression
Re-SchedulingNOIncremental- Type
(Triggered by
(Triggered by Errand- Failure, etc.)
Preference UpdatingNONONONOReinforcement Learning
(to District State
Knowledge Extension of Mental MapNOYes (Long-term
NONONot Yet, but Possible
(Long-Term Adaptation)
PracticalityData-Fitting Methodsby Machine-Learning (C4.5), Automatic Decision-Tree Formingby GA, Non-Linear
Utility Shape Estimation
by Classical Statistical Analysis, Utility and Probability Estimationby Conditional classificationSome by Statistical Analysis, Some by Experiments, Other by Applying Hypothesis
Case StudyReal Cases inc. a Benchmarking Case (of Hendrik-Ido-Ambacht and Zwijndrecht)Numerical IllustrationMany Real Cases for
Practical Uses
Real Case (of
Real Case (of Ohsu, Kanayama, Nagoya)

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