Applying Multi-Agent Systems in the Simulation of Consumer Behavior in Shopping Streets: The Shanghai East Nanjing Road Case

  • 1. Eindhoven University of Technology, Urban Planning Group, Eindhoven 5600MB, The Netherlands;
    2. Department of Urban Planning, Tongji University, Shanghai 200092, China

Received date: 2008-10-15

  Revised date: 2009-01-29

  Online published: 2009-04-25

Supported by

National Natural Science Foundation of China, No.40871080


Modeling consumer behavior has experienced from using aggregate models to individual-based models. During the latest decade, multi-agent technique has been extensively developed with the help of the ever-advancing computer technology. Currently, the method of combining individual-based consumer behavior models and multi-agent simulation systems has become the state-of-the-art method in related research and practical fields. The major advantage of such type of method is that it enables the related professionals to integrate the complexity of various aspects, predict the dynamic processes of the whole or part of the shopping space, and support planning and development based on the behavior of individual consumer. The paper introduces a multi-agent simulation platform developed by the authors based on NetLogo, including the construction of the platform and the procedures of the simulation. The core of the simulation system consists of four major consumer decision models: go-home decision, direction choice decision, rest decision, and store patronage decision, which are constructed differently from conventional choice models based on principles of bounded rationality. The simulation platform is applied to the study of consumer behavior in East Nanjing Road as a validation to the model system. A survey was carried out in the street in 2007. The data recorded the activity diary of consumers and were used for estimating the models. The simulation is carried out using the given distributions of consumers' entry locations and start time, to replicate the observed behavior. Aggregate consumer behavior is more concerned in practice. Therefore, after twenty times of simulation for reducing random fluctuations, individual agent behavior is aggregate. Three aspects of the aggregate simulated behavior are compared with the observed aggregate behavior. The first aspect is the number of consumers conducting different activities overtime, including the total number of consumers in the street, the number of consumers in the stores, the number of consumers taking rests, the number of consumers who are walking, and the number of consumers who have gone home. The second aspect is the number of consumers in street segments over time. The third aspect is the number of consumer visits and duration in individual stores. The results show that in general the simulation platform can predict the aggregate consumer behavior well. Particularly, the activity-time distributions are simulated very well except that the walking behavior is simulated poorly mainly due to the small number of observations. The number of consumers in street segments are simulated not well enough although the general trend in segments with a large number of observations are captured. This means the proposed model system can capture the aspatial behavior better than spatial behavior. This might be due to the fact that the complexity of consumer spatial behavior was affected by many environmental factors and personal variations, and however, it was only modeled using limited explanatory variables. Further studies are needed to be done on consumer behavior in relation with the environment.

Cite this article

ZHU Wei, WANG De, Harry TIMMERMANS . Applying Multi-Agent Systems in the Simulation of Consumer Behavior in Shopping Streets: The Shanghai East Nanjing Road Case[J]. Acta Geographica Sinica, 2009 , 64(4) : 445 -455 . DOI: 10.11821/xb200904007


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