西安建筑科技大学学报(自然科学版)

为探究轨道站点客流时空分布差异及影响因素,以西安市地铁1—6号线为例,结合多源数据构建混合地理加权回归(MGWR)模型,定量分析建成环境及用地特征、交通接驳设施及站点自身属性因素对高峰时段进出站客流的影响.结果表明:西安市轨道站点客流存在显著的空间相关性,同最小二乘(OLS)模型相比,MGWR模型拟合优度更高,能够精确刻画客流与各因素的时空影响关系; 公交线路数、居住用地面积、商业用地面积是轨道站点客流的显著影响因素,受高峰时段和出行行为的影响,不同变量的回归系数存在明显差异; 各影响因素对客流的影响效果呈现不同程度的空间差异,对未来轨道交通周边规划设计提供参考.

In order to explore the temporal and spatial distribution differences and influencing factors of passenger flow at rail stations, this paper takes Xi'an Metro Line 1-6 as an example, constructs a mixed geographically weighted regression(MGWR)model combined with multi-source data, and quantitatively analyzes the influences of built environment, land use characteristics, transport access facilities and station attributes on passenger flow in and out of the station during peak hours. The results show that there is a significant spatial correlation in the passenger flow of Xi'an rail stations. Compared with Ordinary Least Square(OLS)model, MGWR model has higher goodness of fit, and can deeply describes the spatiotemporal influence relationship between passenger flow and various factors. Affected by peak hours and travel behavior, the regression coefficients of different variables are significantly different. The effects of various influencing factors on passenger flow show different degrees of spatial differences, which provides a reference for future planning and design of rail transit. Affected by peak hours and travel behavior, the regression coefficients of different variables vary significantly. The effect of each influencing factor on passenger flow shows different degrees of spatial disparity, which provides a reference for the planning and design of the surrounding areas of rail transit in the future.

引言
1 研究范围及变量选取
2 变量检验及模型构建
3 时空影响因素分析
4 结论

图1 地铁1—6号线站点影响范围划分<br/>Fig.1 Division of station influence scope for Metro Line 1—6

图1 地铁1—6号线站点影响范围划分
Fig.1 Division of station influence scope for Metro Line 1—6

表1 变量的概述性统计<br/>Tab.1 Summary statistics of variables

表1 变量的概述性统计
Tab.1 Summary statistics of variables

表2 变量空间自相关性检验结果<br/>Tab.2 Test results of spatial autocorrelation of variables

表2 变量空间自相关性检验结果
Tab.2 Test results of spatial autocorrelation of variables

图2 因变量局部自相关分析图<br/>Fig.2 Local autocorrelation analysis of dependent variables

图2 因变量局部自相关分析图
Fig.2 Local autocorrelation analysis of dependent variables

表3 多重共线性检验结果<br/>Tab.3 Multicollinearity test results

表3 多重共线性检验结果
Tab.3 Multicollinearity test results

表4 模型运算结果<br/>Tab.4 Model operation results

表4 模型运算结果
Tab.4 Model operation results

表5 各变量回归系数表<br/>Tab.5 Regression coefficient of each variable

表5 各变量回归系数表
Tab.5 Regression coefficient of each variable

图3 早高峰客流各影响因素系数分布<br/>Fig.3 Coefficient distribution of influencing factors for morning peak passenger flow

图3 早高峰客流各影响因素系数分布
Fig.3 Coefficient distribution of influencing factors for morning peak passenger flow

[1] SUNG H, OH J T. Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea[J]. Cities, 2011, 28(1):70-82.
[2]JUN M J, CHOI K, JEONG J E, et al. Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul[J]. Journal of Transport Geography, 2015, 48(OCT.):30-40.
[3]SOHN K, SHIM R. Factors generating boardings at Metro stations in the Seoul metropolitan area[J]. Cities, 2010, 27(5):358-368.
[4]DING C, CHEN P, JIAO J. Non-linear effects of the built environment on automobile-involved pedestrian crash frequency: A machine learning approach[J]. Accident; Analysis and Prevention, 2018, 112:116.
[5]吕帝江. 基于多源地理大数据的地铁客流影响因素研究[D]. 广州:广州大学,2019.
[6]彭诗尧. 基于土地利用的城市轨道交通客流时空特性分析[D]. 北京:北京交通大学,2020.
[7]El-Geneidy A, GRIMSRUD M, VVASFI R, et al. New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas[J]. Transportation, 2014, 41(1):193-210.
[8]甘佐贤. 建成环境对城市轨道交通客流及出行特征的影响机理研究[D]. 南京:东南大学,2019.
[9]王静,刘翔. 城市轨道沿线地区规划设计导则要点研究[J]. 山西建筑,2017,43(4):35-37.
[10]马莉. 城市轨道交通对住宅价格空间效应影响研究[D].西安:西安科技大学,2019.
[11]王剑坡. 城市轨道交通车站客流的影响因素时空异质性研究[D]. 西安:长安大学,2020.
[12]米瑞华,石英.基于常住人口分布的城市主副中心识别方法——以西安市为例[J].陕西师范大学学报(自然科学版),2014,42(3):97-102.
[13]MORANP A P. Notes on continuous stochastic phenomena[J]. Biometrika, 1950,37(1-2):17-23.
[14]钟季汝. 城市建成环境对轨道交通客流特征影响研究[D]. 大连:大连理工大学,2019.
[15]FOTHERINGHAM A S, YANG W, KANG W. Multiscale geographically weighted regression(MGWR)[J]. Annals of the American Association of Geographers, 2017, 107(6):1247-1265.
[16]刘潮汐. 徐州市地铁1号线对沿线住宅价格影响的空间效应研究[D].北京:中国矿业大学,2022.

备注

引言

1 研究范围及变量选取

2 变量检验及模型构建

3 时空影响因素分析

4 结论

期刊信息