交通运输系统工程与信息

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (6): 135-144.DOI: 10.16097/j.cnki.1009-6744.2024.06.012

• 系统工程理论与方法 • 上一篇    下一篇

地铁站属性对共享单车出行影响的异质性研究

刘路1，车宇禄1，朱宇婷*2，周小光3，光志瑞4   

  1. 1. 扬州大学，建筑科学与工程学院，江苏扬州225127；2.北京工商大学，商学院，北京100048； 3. 北京市政路桥建材集团有限公司，北京100164；4.北京市地铁运营有限公司技术创新研究院，北京100039
  • 收稿日期:2024-08-10 修回日期:2024-09-29 接受日期:2024-10-18 出版日期:2024-12-25 发布日期:2024-12-18
  • 作者简介:刘路(1991- )，女，安徽桐城人，讲师，博士。
  • 基金资助:
    江苏省知识管理与智能服务工程研究中心2022年度开放课题/(KMIS202206)；北京市教育委员会科研计划项目资助 (SM202410011001)；国家自然科学基金(52302395)。

Heterogeneity Analysis of Metro Station Attributes Impact on Bike-sharing Trips

LIU Lu1,CHEYulu1,ZHUYuting*2,ZHOU Xiaoguang3,GUANG Zhirui4   

  1. 1. College of Architectural Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China; 2. Business School, Beijing Technology and Business University, Beijing 100048, China; 3. Beijing Municipal Road &Bridge Building Material Group Co Ltd, Beijing 100164, China; 4. Technical Innovation Research Institute of Beijing Mass Transit Railway Operation Co Ltd, Beijing 100039, China
  • Received:2024-08-10 Revised:2024-09-29 Accepted:2024-10-18 Online:2024-12-25 Published:2024-12-18
  • Supported by:
    Jiangsu Knowledge Management and Intelligent Service Engineering Research Center (KMIS202206)； R&D Program of Beijing Municipal Education Commission (SM202410011001)； National Natural Science Foundation of China (52302395)。

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摘要： 基于2018年上海市共享单车订单数据，本文以地铁站周边区域为研究范围，运用多尺度地理加权回归(MGWR)模型，分析建成环境对共享单车出行的影响机理，进一步，从地铁站类型及空间位置两大属性出发，揭示各类因素影响作用的异质性特征。研究结果表明，各类因素对共享单车出行吸引和发生呈现全正向、全负向、正负双重以及正负相反这4种不同的影响效果。面向不同属性的地铁站，影响共享单车出行的主要因素存在明显差异，且该差异在出行发生下更为显著。地铁站距市中心的距离对影响因素重要度排序结果有显著影响，而换乘站和非换乘站影响因素重要度排序方面的差异性相对较小。其中，“地铁站密度大于人口密度大于公交站密度”“居住用地大于地铁站密度”分别是市中心10km范围内出行吸引和发生最主要的影响组合；“人口密度大于公交站密度大于居住用地”“土地信息熵大于人口密度”分别对应市中心10km范围外出行吸引和发生的最主要影响组合。随着地铁站距市中心距离的增加，各类因素的影响系数呈现“<”型、斜“几”字型和“S”型这3类变化趋势，说明建成环境的影响效力随空间位置变化呈复杂的非线性变化特征，地铁站周边共享单车宜采取因地制宜的投放策略。

关键词: 城市交通, 异质性, 多尺度地理加权回归模型, 共享单车, 地铁站

Abstract: Based on the 2018 Shanghai bike-sharing order data, this paper uses the multi-scale geographically weighted regression (MGWR) model to analyze the influence mechanism between the metro built environment and the bike sharing trips. It investigates the heterogeneous characteristics of the influence effects from two major attributes of the metro station type and spatial location. The results show that there are four different types of impacts on the attraction and generation of bike-sharing trips, including, all positive, all negative, double positive or negative, and positive and negative opposite. The main factors influencing bike-sharing trips vary significantly between different metro stations, and this variation becomes even more pronounced when considering trip generation. The distance between metro stations and the city center has a significant impact on the ranking of the key factors while there is marginal difference between transfer stations and non-transfer stations. The scenarios of "Metro Station Density > Population Density > Bus Station Density" and "Residential Land > Metro Station Density" are the most influential combinations of trip attraction and generation within 10 km of the city center. The scenarios of "Population Density > Bus Station Density > Residential Land" and "Land-Use Information Entropy > Population Density" correspond to the most important combinations of trip attraction and generation outside 10 km range from the city center. As the distance of metro stations from the city center increases, the influence coefficients of each factor show different gradual change patterns, such as "<" type, "几" type and "S" type. This indicates that the impact of the built environment on bike-sharing change with the spatial location and there is a complex non-linear change, it is appropriate to adopt a place-specific strategy for bike-sharing around metro stations.

Key words: urban traffic, heterogeneity, multi-scale geographically weighted regression model, bike-sharing, metro station

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