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Influential factors of spatial performance in metro-led urban underground public space:A case study in Shanghai 被引量:1
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作者 Chen-Xiao Ma Fang-Le Peng +1 位作者 Yong-Kang Qiao Hu Li 《Underground Space》 SCIE EI CSCD 2023年第1期229-251,共23页
Spatial performance measures the space usage of each underground segment in metro-led urban underground public space(UUPS).It usually varies in different UUPS segments and at different periods.Many environmental facto... Spatial performance measures the space usage of each underground segment in metro-led urban underground public space(UUPS).It usually varies in different UUPS segments and at different periods.Many environmental factors and space attractors can influence spatial performance in UUPS including spatial configurations,transportation facilities,space design characteristics,and commercial and work-ing facilities.This study intends to figure out the temporal and spatial distribution patterns of spatial performance in UUPS and then reveal the main influential factors and their impact mechanisms.The UUPS in Jiangwan–Wujiaochang Sub-center was selected as the study case.Cordon counting methods and multiple regression models were employed to collect the pedestrian data and quantitatively analyze the correlations between pedestrian flows and candidate influential factors.The study verified that spatial configurations were the most important factors instead of underground or surface attractors.There existed an interactive effect among pedestrian move-ments,spatial configurations,and commerce distribution in metro-led UUPS.Walkway width and the distribution of metro stations could partly affect spatial performance.The influential mechanisms of metro stations were different on weekdays and at weekends.Underground segments belonging to shopping malls in UUPS had a negative impact on spatial performance only on weekdays.Results of this study can provide insights for more efficient layout planning and design of metro-led UUPS in Chinese metropolitan cities. 展开更多
关键词 Urban underground public space Metro-led area Spatial performance Cordon count multiple regression method
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