2007 Fiscal Year Final Research Report Summary
A Study on Integration of Aggregation Type Model and Non-aggregation Type Model in Excursion Behavior of Shopping Pedestrians in the Central Shopping Area
| Project/Area Number |
17560567
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Town planning/Architectural planning
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| Research Institution | Fukuoka University |
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Principal Investigator |
KUROSE Shigeyuki Fukuoka University, Faculty of Eng., Prof. (20153413)
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| Project Period (FY) |
2005 – 2007
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| Keywords | Urban Planning / Pedestrian / Excursion Behavior / Central Shopping Area / Model |
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| Research Abstract |
In 2005, I collected the data at Tenjin district, the central shopping district of Fukuoka, which has underground shopping streets, and made the digital data of them. I also surveyed floor use of the buildings in the study area, and made detailed floor use plan in each floor level.
In 2006, I made the non-aggregation type model and applied it to the data of Tenjin area. First of all, I separated the data into three groups, that is the first one is the group who used only ground level routes, the second one is the group who used both ground and underground level routes, and the last one is the group who used only underground level routes. The data were analyzed in each group. As the results of the analysis of the questionnaire sheets, it was made clear that the route choice behavior were quite different according to age and sex. As the results of appliance of the model, it was made clear that the large ratio of pedestrians who used both ground and underground level, had not-GDM heuristics, and they could not choose the correct routes in their route choices.
After all, the succeeded prediction ratio of the second group is the lowest among three groups. This means that pedestrians who used both ground and underground routes were often covered with confusion to choose their routes.
In 2007, I surveyed public spaces in the study area, and allocated public space area and commercial floor area to each node. I picked up sonic pedestrian who chose the routes near public spaces, and analyzed relationship between attractive factor (public space and commercial floor area) and route choice. After all, some pedestrians did not use minimum paths but used the routes which were allocated more attractive value like public spaces and shopping facilities.
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