Analysis of Metro Station Ridership Considering Spatial Heterogeneity

GAN Zuoxian; FENG Tao; YANG Min; Harry TIMMERMANS; LUO Jinyu

doi:10.1007/s11769-019-1065-8

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

doi: 10.1007/s11769-019-1065-8

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China;
2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

基金项目:

Under the auspices of National Natural Science Foundation of China (No. 71771049), the Six Talent Peaks Project in Jiangsu Province (No. 2016-JY-003), China Scholarship Council (No. 201606090149)

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China;
2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

Funds:

Under the auspices of National Natural Science Foundation of China (No. 71771049), the Six Talent Peaks Project in Jiangsu Province (No. 2016-JY-003), China Scholarship Council (No. 201606090149)

摘要: This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level. Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies. Four models (three global models-ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM) and one local model-geographically weighted regression (GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity. Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China. Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model. The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership. Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance. It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.
- spatial heterogeneity /
- rapid transit ridership /
- built environment /
- station level /
- spatial models
Abstract: This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level. Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies. Four models (three global models-ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM) and one local model-geographically weighted regression (GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity. Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China. Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model. The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership. Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance. It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.
- spatial heterogeneity /
- rapid transit ridership /
- built environment /
- station level /
- spatial models

[1]	Anselin L, Bera A K, Florax R et al., 1996. Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1):77-104. doi:10.1016/0166-0462(95) 02111-6
[2]	Blainey S P, Preston J M, 2013. Extending geographically weighted regression from points to flows:a rail-based case study. Proceedings of the Institution of Mechanical Engineers, Part F:Journal of Rail and Rapid Transit, 227(6):724-734. doi: 10.1177/0954409713496987
[3]	Cardozo O D, García-Palomares J C, Gutiérrez J, 2012. Applica-tion of geographically weighted regression to the direct fore-casting of transit ridership at station-level. Applied Geography, 34:548-558. doi: 10.1016/j.apgeog.2012.01.005
[4]	Cervero R, Ferrell C, Murphy S, 2002. Transit-Oriented Devel-opment and Joint Development in the United States:A Litera-ture Review. Washington, DC:Transportation Research Board.
[5]	Chan S, Miranda-Moreno L, 2013. A station-level ridership model for the metro network in Montreal, Quebec. Canadian Journal of Civil Engineering, 40(3):254-262. doi: 10.1139/cjce-2011-0432
[6]	Choi J, Lee Y J, Kim T et al., 2012. An analysis of Metro ridership at the station-to-station level in Seoul. Transportation, 39(3):705-722. doi: 10.1007/s11116-011-9368-3
[7]	Dill J, Schlossberg M, Ma L et al., 2013. Predicting Transit Rid-ership at the Stop Level:the Role of Service and Urban Form. Washington, DC:Transportation Research Board.
[8]	Durning M, Townsend C, 2015. Direct ridership model of rail rapid transit systems in Canada. Transportation Research Record:Journal of the Transportation Research Board, 2537(1):96-102. doi: 10.3141/2537-11
[9]	Estupiñán N, Rodríguez D A, 2008. The relationship between urban form and station boardings for Bogota's BRT. Trans-portation Research Part A:Policy and Practice, 42(2):296-306. doi: 10.1016/j.tra.2007.10.006
[10]	Fotheringham A S, Brunsdon C, Charlton M E, 2003. Geograph-ically Weighted Regression:the Analysis of Spatially Varying Relationships. Chichester:Wiley.
[11]	Gan Z, Yang M, Feng T et al., 2018. Understanding urban mobility patterns from a spatiotemporal perspective:daily ridership profiles of metro stations. Transportation. doi: 10.1007/s11116-018-9885-4
[12]	Guerra E, Cervero R, Tischler D, 2012. Half-mile circle:does it best represent transit station catchments? Transportation Re-search Record:Journal of the Transportation Research Board, 2276(1):101-109. doi: 10.3141/2276-12
[13]	Gutiérrez J, Cardozo O D, García-Palomares J C, 2011. Transit ridership forecasting at station level:an approach based on dis-tance-decay weighted regression. Journal of Transport Geogra-phy, 19(6):1081-1092. doi: 10.1016/j.jtrangeo.2011.05.004
[14]	Jun M J, Choi K, Jeong J E et al., 2015. Land use characteristics of subway catchment areas and their influence on subway rid-ership in Seoul. Journal of Transport Geography, 48:30-40. doi: 10.1016/j.jtrangeo.2015.08.002
[15]	Kepaptsoglou K, Stathopoulos A, Karlaftis M G, 2017. Ridership estimation of a new LRT system:direct demand model ap-proach. Journal of Transport Geography, 58:146-156. doi: 10.1016/j.jtrangeo.2016.12.004
[16]	Kim D, Ahn Y, Choi S et al., 2016. Sustainable mobility:longitu-dinal analysis of built environment on transit ridership. Sus-tainability, 8(10):1016. doi: 10.3390/su8101016
[17]	Kuby M, Barranda A, Upchurch C, 2004. Factors influencing light-rail station boardings in the United States. Transportation Research Part A:Policy and Practice, 38(3):223-247. doi: 10.1016/j.tra.2003.10.006
[18]	Lloyd C, Shuttleworth I, 2005. Analysing commuting using local regression techniques:scale, sensitivity, and geographical pat-terning. Environment and Planning A:Economy and Space, 37(1):81-103. doi: 10.1068/a36116
[19]	Loo B P Y, Chen C, Chan E T H, 2010. Rail-based transit-oriented development:lessons from New York City and Hong Kong. Landscape and Urban Planning, 97(3):202-212. doi: 10.1016/j.landurbplan.2010.06.002
[20]	Macdonald-Wallis K, Jago R, Page A S et al., 2011. School-based friendship networks and children's physical activity:a spatial analytical approach. Social Science & Medicine, 73(1):6-12. doi: 10.1016/j.socscimed.2011.04.018
[21]	Mason R L, Gunst R F, Hess J L, 1989. Statistical Design and Analysis of Experiments:with Applications to Engineering and Science. New York:Wiley.
[22]	McNally M G, 2007. The four-step model. In:Hensher D A, But-ton K J (eds). Handbook of Transport Modelling. Oxford:Pergamon, 35-53.
[23]	Ministry of Housing and Urban-Rural Development, 2012. Guidelines for Planning and Design of Urban Rail. Beijing:China Construction Industry Publishing House. (in Chinese)
[24]	Páez A, 2006. Exploring contextual variations in land use and transport analysis using a probit model with geographical weights. Journal of Transport Geography, 14(3):167-176. doi: 10.1016/j.jtrangeo.2005.11.002
[25]	Pavlyuk D, 2016. Implication of spatial heterogeneity for airports' efficiency estimation. Research in Transportation Economics, 56:15-24. doi: 10.1016/j.retrec.2016.07.002
[26]	Pulugurtha S S, Agurla M, 2012. Assessment of models to esti-mate bus-stop level transit ridership using spatial modeling methods. Journal of Public Transportation, 15(1):33-52. doi: 10.5038/2375-0901.15.1.3
[27]	Rasouli S, Timmermans H, 2014. Activity-based models of travel demand:promises, progress and prospects. International Journal of Urban Sciences, 18(1):31-60. doi: 10.1080/12265934.2013.835118
[28]	Ryan S, Frank L F, 2009. Pedestrian environments and transit ridership. Journal of Public Transportation, 12(1):39-57. doi: 10.5038/2375-0901.12.1.3
[29]	Sohn K, Shim H, 2010. Factors generating boardings at metro stations in the Seoul metropolitan area. Cities, 27(5):358-368. doi: 10.1016/j.cities.2010.05.001
[30]	Sung H, Oh J T, 2011. Transit-oriented development in a high-density city:identifying its association with transit rid-ership in Seoul, Korea. Cities, 28(1):70-82. doi: 10.1016/j.cities.2010.09.004
[31]	Tobler W R, 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(S1):234-240.
[32]	Zhang D P, Wang X K, 2014. Transit ridership estimation with network Kriging:a case study of Second Avenue Subway, NYC. Journal of Transport Geography, 41:107-115. doi: 10.1016/j.jtrangeo.2014.08.021
[33]	Zhao J B, Deng W, Song Y et al., 2013. What influences metro station ridership in China? Insights from Nanjing. Cities, 35:114-124. doi: 10.1016/j.cities.2013.07.002
[34]	Zhao J B, Deng W, Song Y et al., 2014. Analysis of Metro rid-ership at station level and station-to-station level in Nanjing:an approach based on direct demand models. Transportation, 41(1):133-155. doi: 10.1007/s11116-013-9492-3

[1]	Xiaoyan HUANG, Chenchen KANG, Chun YIN, Junqing TANG. Influence of Transportation Accessibility on Urban-rural Income Disparity and Its Spatial Heterogeneity . Chinese Geographical Science, 2024, (): 1-15. doi: 10.1007/s11769-024-1427-8
[2]	Yang WANG, Kangmin WU, Hong’ou ZHANG, Yi LIU, Xiaoli YUE. Identifying Spatial Heterogeneity in the Effects of High-Tech Firm Density on Housing Prices: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area, China . Chinese Geographical Science, 2023, 33(2): 233-249. doi: 10.1007/s11769-023-1341-5
[3]	Yun TONG, Li PANG, Minghui SUN, Junyi HAN. Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China: Based on Spatial Heterogeneity Perspectives . Chinese Geographical Science, 2023, 33(6): 1109-1126. doi: 10.1007/s11769-023-1378-5
[4]	Jingjing TANG, Kangkang GU, Jing MI, Wenhao ZHANG, Yunhao FANG, Yuwei LI, Beichen WANG. Spatio-temporal Distribution Characteristics and Environmental Impact Factors of Lung Cancer Mortality: A Case Study of Yuhui District in Bengbu City, China . Chinese Geographical Science, 2022, 32(5): 867-882. doi: 10.1007/s11769-022-1305-1
[5]	Bo WANG, Yaqin LEI, Desheng XUE, Jixiang LIU, Chunzhu WEI. Elaborating Spatiotemporal Associations Between the Built Environment and Urban Vibrancy: A Case of Guangzhou City, China . Chinese Geographical Science, 2022, 32(3): 480-492. doi: 10.1007/s11769-022-1272-6
[6]	Kangmin WU, Yang WANG, Hong’ou ZHANG, Yi LIU, Yuyao YE. Impact of the Built Environment on the Spatial Heterogeneity of Regional Innovation Productivity: Evidence from the Pearl River Delta, China . Chinese Geographical Science, 2021, 31(3): 413-428. doi: 10.1007/s11769-021-1198-4
[7]	ZHANG Yuan, LIU Shaomin, HU Xiao, WANG Jianghao, LI Xiang, XU Ziwei, MA Yanfei, LIU Rui, XU Tongren, YANG Xiaofan. Evaluating Spatial Heterogeneity of Land Surface Hydrothermal Con-ditions in the Heihe River Basin . Chinese Geographical Science, 2020, 30(5): 855-875. doi: 10.1007/s11769-020-1151-y
[8]	YANG Jing, CHENG Changxiu, SONG Changqing, SHEN Shi, ZHANG Ting, NING Lix-in. Spatial-temporal Distribution Characteristics of Global Seismic Clusters and Associated Spatial Factors . Chinese Geographical Science, 2019, 20(4): 614-625. doi: 10.1007/s11769-019-1059-6
[9]	QIU Fangdao, YUAN He, BAI Liangyu, LI Fei. Spatial-temporal Heterogeneity of Industrial Structure Transformation and Carbon Emission Effects in Xuzhou Metropolitan Area . Chinese Geographical Science, 2017, 27(6): 904-917. doi: 10.1007/s11769-017-0920-8
[10]	LI Jing, LO Kevin, ZHANG Pingyu, GUO Meng. Relationship Between Built Environment, Socio-economic Factors and Carbon Emissions from Shopping Trip in Shenyang City, China . Chinese Geographical Science, 2017, 27(5): 722-734. doi: 10.1007/s11769-017-0904-8
[11]	YU Lingxue, ZHANG Shuwen, LIU Tingxiang, TANG Junmei, BU Kun, YANG Jiuchun. Spatio-temporal Pattern and Spatial Heterogeneity of Ecotones Based on Land Use Types of Southeastern Da Hinggan Mountains in China . Chinese Geographical Science, 2015, 25(2): 184-197. doi: 10.1007/s11769-014-0671-8
[12]	DU Hu, WANG Kelin, PENG Wanxia, ZENG Fuping, SONG Tongqing, ZHANG Hao, LU Shiyang. Spatial Heterogeneity of Soil Mineral Oxide Components in Depression Between Karst Hills, Southwest China . Chinese Geographical Science, 2014, 0(2): 163-179. doi: 10.1007/s11769-013-0630-9
[13]	XIA Jun, CHEN Junxu, WENG Jianwu, YU Lei, QI Junyu, LIAO Qiang. Vulnerability of Water Resources and Its Spatial Heterogeneity in Haihe River Basin, China . Chinese Geographical Science, 2014, 0(5): 525-539. doi: 10.1007/s11769-014-0720-3
[14]	QIU Bingwen, ZENG Canying, CHENG Chongcheng, TANG Zhenghong, GAO Jianyang, SUI Yinpo. Characterizing Landscape Spatial Heterogeneity in Multisensor Images with Variogram Models . Chinese Geographical Science, 2014, 0(3): 317-327. doi: 10.1007/s11769-013-0649-y
[15]	DONG Minghui, ZOU Bin, PU Qiang, WAN Neng, YANG Lingbin, LUO Yanqing. Spatial Pattern Evolution and Casual Analysis of County Level Economy in Changsha-Zhuzhou-Xiangtan Urban Agglomeration, China . Chinese Geographical Science, 2014, 0(5): 620-630. doi: 10.1007/s11769-014-0685-2
[16]	ZHAO Ying, CHAI Yanwei. Residents’ Activity-travel Behavior Variation by Communities in Beijing, China . Chinese Geographical Science, 2013, 23(4): 492-505. doi: 10.1007/s11769-013-0616-7
[17]	WEN Zhaofei, ZHANG Ce, ZHANG Shuqing, et al. Effects of Normalized Difference Vegetation Index and Related wavebands′ Characteristics on Detecting Spatial Heterogeneity Using Variogram-based Analysis . Chinese Geographical Science, 2012, 22(2): 188-195.
[18]	SHI Xiaoliang, LI Ying, DENG Rongxin. The Method Study of Spatial heterogeneity evaluation on the Landscape Pattern Of Farmland Shelterbelt Network . Chinese Geographical Science, 2011, 21(1): 48-56.
[19]	Jim Pooler. COMPETITION AMONG DESTINATIONS IN SPATIAL INTERACTION MODELS: A NEW POINT OF VIEW . Chinese Geographical Science, 1998, 8(3): 212-224.
[20]	费洪平. MODELS OF THE SPATIAL EVOLUTION OF MULTI-PLANT ENTERPRISES . Chinese Geographical Science, 1994, 4(2): 134-147.

点击查看大图

计量

文章访问数: 338
HTML全文浏览量: 13
PDF下载量: 178
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

doi: 10.1007/s11769-019-1065-8

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

计量

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

doi: 10.1007/s11769-019-1065-8

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China;

2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

English Abstract

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China;

2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

全文HTML

目录

留言板

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

doi: 10.1007/s11769-019-1065-8

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

计量

出版历程

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

doi: 10.1007/s11769-019-1065-8

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China; 2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

English Abstract

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China; 2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

全文HTML

目录

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China;

2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands

1. Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 211189, China;

2. Urban Planning Group, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands