地铁站点多时间维度客流影响因素的精细建模——以广州市中心城区为例
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摘要
当前我国地铁规划建设进入了快速发展阶段,地铁站点客流影响因素的精细建模与分析对站点的规划、管理具有重要意义。该文基于地铁刷卡数据、高分辨率影像及网络爬虫获取的POIs数据、互联网房产数据和建筑物轮廓高度数据等多源空间数据,采用向后逐步回归分析法对广州市中心城区地铁站点多时间维度上客流的影响因素进行精细建模与分析。研究发现:1)整体上,地铁1号线早进晚出客流较其他线路大;站点早出晚进客流集聚效应显著,主要集中在以体育西路为中心的天河中心圈和以公园前为中心的老城区中心圈。2)在所构建的8种客流回归模型中,CBD虚拟变量、商业用地、三类居住用地、进出站口数量和站点吸引范围内平均小区容积率5种因素对站点客流具有显著正向影响,其中CBD虚拟变量影响最大,而房价、水体对客流具有显著负向影响。3)工作日和休息日客流影响因素存在明显差异,二类居住用地、教育科研用地和医疗卫生用地只在工作日客流中具有显著正向影响,用地多样性只在休息日客流中具有显著的负向影响。4)二类居住用地与工作日早进晚出客流关系密切;站点吸引范围内平均建筑层数只在工作日早出晚进客流回归模型中保留并起显著作用。
Metro lines and services are rapidly growing in China.The purpose of this study is to build a fine-scale model to analyze factors that influence transit ridership at multi-time dimensions within the pedestrian catchment area(PCA) of transit stations in Guangzhou,China.This study was based on multi-source spatial data,including smart card data,high spatial resolution images,points of interest(POIs),real-estate online data and building height data.Eight multiple linear regression models using the backward stepwise method and the geographic information system(GIS) were built at the station level.The findings are as follows.1) The average ridership of morning peak inbound and evening peak outbound on Line No.1 was larger than other lines.The average ridership of morning peak outbound and evening peak inbound clustered significantly.They were both highly concentrated in the Tiyu Xilu MRT(Mass Rapid Transit) of Tianhe downtown area and Gongyuanqian MRT of old downtown area.2) Five factors were found to be significantly correlated with station transit ridership positively,which include CBD dummy variable,commercial land use,the area of the third residential land use,number of entrance or exit and average community volume rate of PCA.The average housing price of PCA and the area of water land use,however,played a negative role in it instead.3) There are obvious differences in the factors affecting transit ridership on weekdays and weekends.On the one hand,the area of the second residential land use,educational research land use and medical land use were found to be significantly correlated with transit ridership positively on weekdays,but non-related over weekends.On the other hand,diversity of land use had a significant impact on the passenger flow on weekends,but non-related on weekdays.4) The second residential land use was highly associated with the average ridership of morning peak inbound and evening peak outbound.The average number of building storey within the PCA only played a significant role in the average morning peak outbound and evening peak inbound ridership model.Multi-source spatial data provide a new way for the fine-scale modeling and analysis of the influencing factors of passenger flow.The findings can be useful for station planning,management and policy-making.
Metro lines and services are rapidly growing in China.The purpose of this study is to build a fine-scale model to analyze factors that influence transit ridership at multi-time dimensions within the pedestrian catchment area(PCA) of transit stations in Guangzhou,China.This study was based on multi-source spatial data,including smart card data,high spatial resolution images,points of interest(POIs),real-estate online data and building height data.Eight multiple linear regression models using the backward stepwise method and the geographic information system(GIS) were built at the station level.The findings are as follows.1) The average ridership of morning peak inbound and evening peak outbound on Line No.1 was larger than other lines.The average ridership of morning peak outbound and evening peak inbound clustered significantly.They were both highly concentrated in the Tiyu Xilu MRT(Mass Rapid Transit) of Tianhe downtown area and Gongyuanqian MRT of old downtown area.2) Five factors were found to be significantly correlated with station transit ridership positively,which include CBD dummy variable,commercial land use,the area of the third residential land use,number of entrance or exit and average community volume rate of PCA.The average housing price of PCA and the area of water land use,however,played a negative role in it instead.3) There are obvious differences in the factors affecting transit ridership on weekdays and weekends.On the one hand,the area of the second residential land use,educational research land use and medical land use were found to be significantly correlated with transit ridership positively on weekdays,but non-related over weekends.On the other hand,diversity of land use had a significant impact on the passenger flow on weekends,but non-related on weekdays.4) The second residential land use was highly associated with the average ridership of morning peak inbound and evening peak outbound.The average number of building storey within the PCA only played a significant role in the average morning peak outbound and evening peak inbound ridership model.Multi-source spatial data provide a new way for the fine-scale modeling and analysis of the influencing factors of passenger flow.The findings can be useful for station planning,management and policy-making.
引文
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[15] 吴堑虹,刘琼,段雪刚.土地利用结构指标新探及计算程序研究[J].地理与地理信息科学,2015,31(1):110-114.
[16] 齐兰兰,周素红.邻里建成环境对居民外出型休闲活动时空差异的影响——以广州市为例[J].地理科学,2018,38(1):31-40.
[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,2015,48:30-40.
[3] SOHN K,SHIM H.Factors generating boardings at metro stations in the Seoul metropolitan area[J].Cities,2010,27(5):358-368.
[4] CHOI J,YONG J L,KIM T,et al.An analysis of metro ridership at the station-to-station level in Seoul[J].Transportation,2012,39(3):705-722.
[5] ZHAO J,DENG W,SONG Y,et al.What influences metro station ridership in China?Insights from Nanjing[J].Cities,2013,35(4):114-124.
[6] SUN L S,WANG S W,YAO L Y,et al.Estimation of transit ridership based on spatial analysis and precise land use data[J].Transportation Letters,2016,8(3):140-147.
[7] 王淑伟.站点周边用地特性对轨道客流影响机理研究[D].北京:北京工业大学,2015.
[8] 王淑伟,孙立山,郝思源,等.基于精细化用地的轨道客流直接估计模型[J].交通运输系统工程与信息,2015,15(3):37-43.
[9] KUBY M,BARRANDA A,UPCHURCH C.Factors influencing light-rail station boardings in the United States[J].Transportation Research (Policy and Practice),2004,38(3):223-247.
[10] CARDOZO O D,GARCíA-PALOMARES J C,GUTIéRREZ J.Application of geographically weighted regression to the direct forecasting of transit ridership at station-level[J].Applied Geography,2012,34(4):548-558.
[11] 田永中,陈述彭,岳天祥,等.基于土地利用的中国人口密度模拟[J].地理学报,2004,59(2):283-292.
[12] 杨昆,杨玉莲,朱彦辉,等.中国PM2.5污染与社会经济的空间关系及成因[J].地理研究,2016,35(6):1051-1060.
[13] 张景哲,刘启明.北京城市气温与下垫面结构关系的时相变化[J].地理学报,1988,29(2):159-168.
[14] 谭章智,李少英,黎夏,等.城市轨道交通对土地利用变化的时空效应[J].地理学报,2017,72(5):850-862.
[15] 吴堑虹,刘琼,段雪刚.土地利用结构指标新探及计算程序研究[J].地理与地理信息科学,2015,31(1):110-114.
[16] 齐兰兰,周素红.邻里建成环境对居民外出型休闲活动时空差异的影响——以广州市为例[J].地理科学,2018,38(1):31-40.