طراحي فضاي داخلي فروشگاه‌ها بر اساس شبيه‌‌سازي الگوي حركتي افراد

Designing Interior Stores’ Space Based on Simulating Individuals’ Movement Patterns

ﻫﺪف ﭘﮋوﻫﺶ ﻃﺮاﺣﯽ ﻓﻀﺎي داﺧﻠﯽ ﻓﺮوﺷﮕﺎهﻫﺎ ﺑﺮاﺳﺎس ﺷـﺒ ﯿﻪﺳـﺎزي اﻟﮕـﻮي ﺣﺮﮐﺘـﯽ اﻓـﺮاد اﺳﺖ. ﺟﺎﻣﻌﻪ آﻣﺎري ﭘﮋوﻫﺶ ﺷﺎﻣﻞ ﻣﺸﺘﺮﯾﺎن ﻓﺮوﺷﮕﺎه ﻫﺎﯾﭙﺮﻣﺎرﮐﺖ ﻗﻘﻨـﻮس در ﺷـﻬﺮ ﺳـﻤﻨﺎن اﺳﺖ. دادهﻫﺎ از ﻃﺮﯾﻖ ﻓﯿﻠﻢﻫﺎي دورﺑﯿﻦ ﻣﺪار ﺑﺴﺘﻪ ﻓﺮوﺷﮕﺎه ﮔﺮدآوري و ﺑﺮاي ﻃﺮاﺣﯽ ﻓﻀـﺎي داﺧﻠﯽ ﭼﻨﺪﯾﻦ ﮔﺎم ﻃﯽ ﺷﺪ. در ﮔﺎم اول ﺑﺎ اﺳﺘﻔﺎده از ﺗﮑﻨﯿﮏ ﻃﺮح ﻫﻤﺪﻟﯽ، رﻓﺘﺎر ﻣﺸﺘﺮﯾﺎن درون ﻓﺮوﺷﮕﺎه آﻧﺎﻟﯿﺰ ﺷﺪ. در ﮔﺎم دوم ﺑﺎ اﺳﺘﻔﺎده از ﻧﺮماﻓـﺰار ﻃﺮاﺣـﯽ اﺗﻮﮐـﺪ ﻓﻀـﺎي ﻓﺮوﺷـﮕﺎه ﺑـﺎ ﭼﯿﺪﻣﺎنﻫﺎي ﻗﺎﻟﺐ اﺳﺘﺎﻧﺪارد و ﺑﺎ اﺳﺘﻔﺎده از روش ﮔﺮوه ﮐﺎﻧﻮﻧﯽ، ﭼﯿـﺪﻣﺎﻧﯽ ﺑـﺮ اﺳـﺎس ﻧﻈـﺮات ﻣﺸﺘﺮﯾﺎن ﻃﺮاﺣﯽ ﺷﺪ. در ﮔﺎم ﺳـﻮم ﺑـﺎ اﺳـﺘﻔﺎده از روش ﺷـﺒﯿﻪ ﺳـﺎزي ﻓﻀـﺎي ﻓﺮوﺷـﮕﺎه ﺑـﺎ ﭼﯿﺪﻣﺎنﻫﺎي ﻣﺨﺘﻠﻒ ﻃﺮاﺣﯽ و اﻟﮕﻮي ﺣﺮﮐﺘﯽ اﻓﺮاد در آن ﺷﺒﯿﻪﺳﺎزي ﺷـﺪ و در ﮔـﺎم آﺧـﺮ ﺑـﺎ اﺳﺘﻔﺎده از روش رﮔﺮﺳﯿﻮن ﺧﻄﯽ، ﺗﻌﺪاد اﻗﻼم ﺧﺮﯾﺪاري ﺷﺪه ﺑﺮاي ﻫﺮ ﺳﻨﺎرﯾﻮي ﺗﻌﺮﯾﻒ ﺷﺪه، ﭘﯿﺶﺑﯿﻨﯽ و ﺑﺎ ﻫﻢ ﻣﻘﺎﯾﺴﻪ ﺷﺪﻧﺪ. ﻧﺘﺎﯾﺞ ﺗﺤﻠﯿﻞ رﮔﺮﺳﯿﻮن ﻧﺸﺎن داد ﮐﻪ ﻣﺴﺎﻓﺖ ﻃﯽ ﺷﺪه، ﺳﺮﻋﺖ ﺣﺮﮐﺖ، زﻣﺎن ﺧﺮﯾﺪ و ﻧﻮع رﻓﺘﺎر ﻣﺸﺘﺮﯾﺎن ﻋﻮاﻣﻞ ﻣﻮﺛﺮ ﺑﺮ ﭘﯿﺶﺑﯿﻨﯽ ﺗﻌﺪاد اﻗﻼم ﺧﺮﯾﺪاري ﺷﺪه، ﻫﺴﺘﻨﺪ و ﻧﺘﺎﯾﺞ ﻣﻘﺎﯾﺴﻪ ﺳﻨﺎرﯾﻮﻫﺎي ﻃﺮحﺑﻨﺪي ﻓﺮوﺷﮕﺎه ﻧﺸﺎن داد ﮐﻪ ﺑﻌﺪ از ﭼﯿﺪﻣﺎن ﺑـﺎ ﻣﺴـﯿﺮ اﺟﺒﺎري، ﭼﯿﺪﻣﺎن ﻃﺮاﺣﯽ ﺷﺪه ﺑﺎ ﻣﺸﺎرﮐﺖ ﻣﺸﺘﺮﯾﺎن، ﺑﯿﺸـﺘﺮﯾﻦ ﺗﻐﯿﯿـﺮات را در اﻓـﺰاﯾﺶ زﻣـﺎن ﺧﺮﯾﺪ )12%(، ﻣﺴﺎﻓﺖ ﻃﯽ ﺷﺪه )22%( و ﺗﻌﺪاد اﻗﻼم ﺧﺮﯾﺪاري ﺷﺪه )18%( دارد.

The main purpose of this study is to designing interior stores’ space based on simulating individuals’ movement patterns. On this basis, it is exploratory in terms of objective and applied in terms of results. The statistical population of the study included customers of Ghoghnoos hypermarket in Semnan and data collected using CCTV films of a hypermarket. Several steps were taken to design the interior. In the first step, the in-store customer behavior was analyzed using empathic design techniques. In the second step, using AutoCAD design software, the store space was designed with standard template layouts and, using the focus group approach, a layout based on customer feedback was designed. In the third step, using the simulation method, different store layouts were designed and the people's movement pattern was simulated and Finally, using linear regression, the number of sales items for each scenario was defined, predicted and compared. The results of linear regression analysis showed that distance traveled, speed of movement, shopping time and type of customer behavior are factors influencing basket size forecasting. The results of comparing different store layout scenarios showed that after forced-path layout, layout designed with customer involvement, had the highest percentage of changes in increasing purchase time (12%), distance traveled (22%) and basket size (18%).