地铁站点客流量影响因素及空间异质性分析
Analysis of Influencing Factors and Spatial Heterogeneity of Passenger Flow at Metro Stations
张毅萌 1郑乐 1朱岑远1
作者信息
- 1. 南京邮电大学现代邮政学院,南京 210003
- 折叠
摘要
为探究地铁站点客流量影响因素及其空间异质性对站点客流量的影响,以南京地铁站点为研究对象,使用泰森多边形划分站点服务范围,识别站点服务范围内的POI、路网密度、居住人口等影响因素,对自变量进行了空间 自相关分析和多重共线性分析,构建了基于多源数据的OLS、GWR、MGWR模型并比较了不同模型的结果.研究结果表明,站点的度、站点周边的路网密度为局部变量,站点周边的居住人口、共享单车使用量为全局变量.得出研究结论:MGWR模型在可解释性和拟合度上优于OLS和GWR模型;在人口密度较大且路网密度未饱和的区域内提高路网密度可以有效提高该区域内的地铁站点客流量;在人口密度较大的区域内增设接驳公交线路可有效提高公交系统内部衔接的便捷度;建议优先在地铁站点客流量较低的区域加大共享单车投放量以提高地铁站点客流量.
Abstract
To explore the factors influencing the passenger flow of metro stations and the spatial heterogeneity of these factors,this study focuses on the metro stations in Nanjing.Thiessen polygons are used to delineate the service areas of the stations and identify the influencing factors within these areas,including Points of Interest(POI),road network density,and resident population.Spatial autocorrelation analysis and multicollinearity analysis are conducted on the independent variables.OLS,GWR,and MGWR models are constructed based on multiple data sources,and the results of different models are compared.The findings indicate that the degree of the station and the road network density around the station are local variables,while the resident population and shared bicycle usage around the station are global variables.The MG-WR model outperforms the OLS and GWR models in terms of interpretability and fit.Increasing the road network density in areas with high population density and unsaturated road network density can effectively improve the passenger flow of metro stations in those areas.Adding connecting bus routes in areas with high population density can enhance the conven-ience of interconnection within the public transportation system.It is recommended to increase the number of shared bicy-cles in areas with low passenger flow at metro stations to improve the passenger flow.
关键词
公共交通/地铁站点客流量/空间异质性/多尺度地理加权回归模型/泰森多边形Key words
mass transit/metro station passenger flow/spatial heterogeneity/multiscale geographic weighted regression/thiessen polygon引用本文复制引用
基金项目
国家自然科学基金(52102381)
中国博士后科学基金(2023M731778)
江苏省高等学校哲学社会科学一般项目(TJZ221042)
出版年
2024
NETL
NSTL
万方数据