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杭州轨道交通地下商业布局研究 被引量:4

A Study on the Underground Commercial Layout of Hangzhou Rail Transit Stations
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摘要 城市轨道交通地下商业空间布局是提升轨道交通整体品质的重要环节,跟踪人流消费行为足迹可以有效地解析空间与人流行为的互动关系。选取杭州3个典型轨道交通站点,利用时间取样法和行人计数法对人流量进行统计,计算得出不同业态空间的吸引力权重,进而采用空间句法Depthmap软件得到空间属性数据,并对影响因素进行多元线性回归分析。结果显示,休闲娱乐空间的经济、人流集聚力影响显著,不受空间属性影响;餐饮、服饰鞋包等业态依赖于空间整合度。最后,对商业类型、服务类型、交通枢纽类型轨道交通站点提出商业布局优化对策建议。 A proper underground commercial layout of urban rail transit stations plays an important role in upgrading the rail transit system and the interrelation between space and passengers, and it can be effectively explained by tracking passengers' consumption behaviors. This paper first obtains the passenger numbers at three typical rail transit stations in Hangzhou by applying the methods of time sampling and passenger counting, based on the attraction weights of space for different commercial types which are also calculated. Next this paper acquires the spatial attributes with Depthmap, a space syntax software, and carried out multiple linear regression analysis of the influencing factors. The results show that the space for leisure and recreation is not affected by spatial attributes which enjoys prominent economic agglomeration and attraction for passengers, while the space for other commercial types, such as catering and retailing of clothes, shoes and bags, is dependent on spatial integration degree to some extent. This paper finally proposes the measures to optimize the layout of commercial space at urban rail transit stations in terms of commercial types and service types for different transportation hubs in Hangzhou.
作者 王奕苏 叶诗宇 金园 石坚韧 WANG Yi-su;YE Shi-yu;JIN Yuan;SHI Jian-ren(School of Tourism and Urban-rural Planning, Zhejiang Gongshang University, Hangzhou 310008, Zhejiang, China)
出处 《铁道运输与经济》 北大核心 2018年第6期95-100,共6页 Railway Transport and Economy
基金 国家自然科学基金项目(51408545) 浙江工商大学国家级大学生创新创业训练项目(GJ201605001) 浙江工商大学高等教育研究课题(Xgy17024) 浙江工商大学2015年度课堂教学创新项目(XKTCX201505)
关键词 城市轨道交通 地下商业布局 优化布局 整合度 功能吸引 回归模型 Urban Rail Transit Underground Commercial Layout Layout Optimization Integration Degree Functional Attraction Regression Models
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