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Disaggregate Hierarchical Decision Huff Model Incorporating Consumer Kaiyu Choices Among Shopping Sites Read chapter Read first chapter

2023 | OriginalPaper | Chapter

A Bayesian Network Model of Consumers’ Kaiyu Behaviors

Authors : Rui Yamaguchi, Saburo Saito

Published in: Recent Advances in Modeling and Forecasting Kaiyu

Publisher: Springer Nature Singapore

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Abstract

A consumer walking around a downtown shopping area usually visits several places and shops. From the point of view of city management, it is crucial to know and predict people’s movements in the area, for example, to plan to make better pathways to enhance mobility. From a shop manager’s point of view, it is also vital to grasp the tendency of people visiting other shops to identify whether those shops are competing or cooperating with his/her shop. In order to offer helpful information for these tasks, we used graphical models called Bayesian networks in this research. By applying the models to actual data obtained from questionnaire surveys of consumers who visited a downtown shopping area, we analyzed consumers’ shop-around or Kaiyu behaviors.

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previous chapter Extraction of Long Sightseeing Kaiyu Routes in the Kyushu Wide Region, Japan
Footnotes
1
For more details, see Saito [30].
 
2
Refer to the references [2‐5] for more details about the introduction and learning algorithm for BNs.
 
3
Refer to Ando [51] for the original starting form of the survey and see Saito and Yamashiro [10, 11] for the present form of the survey.
 
4
Refer to [16, 17]. Also refer to a recent volume of this series, Saito and Yamashiro [52] and Saito and Ishibashi [18], Chap. 5 in this volume.
 
5
See [2] for details for parameter learning.
 
6
This is called human modeling by Motomura [55].
 
7
See also [56].
 
8
The original version of this chapter first appeared in Yamaguchi and Saito [57].
 
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Metadata
Title
A Bayesian Network Model of Consumers’ Kaiyu Behaviors
Authors
Rui Yamaguchi
Saburo Saito
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Recent Advances in Modeling and Forecasting Kaiyu

Print ISBN: 978-981-9912-40-7

Electronic ISBN: 978-981-9912-41-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-99-1241-4_14