Advertisement

Chinese Geographical Science

, Volume 29, Issue 6, pp 1065–1077 | Cite as

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

  • Zuoxian Gan
  • Tao Feng
  • Min YangEmail author
  • Harry Timmermans
  • Jinyu Luo
Article
  • 25 Downloads

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.

Keywords

spatial heterogeneity rapid transit ridership built environment station level spatial models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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:  https://doi.org/10.1016/0166-0462(95)02111-6 CrossRefGoogle Scholar
  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:  https://doi.org/10.1177/0954409713496987 CrossRefGoogle Scholar
  3. Cardozo O D, García-Palomares J C, Gutiérrez J, 2012. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34: 548–558. doi:  https://doi.org/10.1016/j.apgeog.2012.01.005 CrossRefGoogle Scholar
  4. Cervero R, Ferrell C, Murphy S, 2002. Transit-Oriented Development and Joint Development in the United States: A Literature Review. Washington, DC: Transportation Research Board.Google Scholar
  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:  https://doi.org/10.1139/cjce-2011-0432 CrossRefGoogle Scholar
  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:  https://doi.org/10.1007/s11116-011-9368-3 CrossRefGoogle Scholar
  7. Dill J, Schlossberg M, Ma L et al., 2013. Predicting Transit Ridership at the Stop Level: the Role of Service and Urban Form. Washington, DC: Transportation Research Board.Google Scholar
  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:  https://doi.org/10.3141/2537-11 CrossRefGoogle Scholar
  9. Estupiñán N, Rodríguez D A, 2008. The relationship between urban form and station boardings for Bogota’s BRT. Transportation Research Part A: Policy and Practice, 42(2): 296–306. doi:  https://doi.org/10.1016/j.tra.2007.10.006 Google Scholar
  10. Fotheringham A S, Brunsdon C, Charlton M E, 2003. Geographically Weighted Regression: the Analysis of Spatially Varying Relationships. Chichester: Wiley.Google Scholar
  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:  https://doi.org/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 Research Record: Journal of the Transportation Research Board, 2276(1): 101–109. doi:  https://doi.org/10.3141/2276-12 CrossRefGoogle Scholar
  13. Gutiérrez J, Cardozo O D, García-Palomares J C, 2011. Transit ridership forecasting at station level: an approach based on distance-decay weighted regression. Journal of Transport Geography, 19(6): 1081–1092. doi:  https://doi.org/10.1016/j.jtrangeo.2011.05.004 CrossRefGoogle Scholar
  14. Jun M J, Choi K, Jeong J E et al., 2015. Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul. Journal of Transport Geography, 48: 30–40. doi:  https://doi.org/10.1016/j.jtrangeo.2015.08.002 CrossRefGoogle Scholar
  15. Kepaptsoglou K, Stathopoulos A, Karlaftis M G, 2017. Ridership estimation of a new LRT system: direct demand model approach. Journal of Transport Geography, 58: 146–156. doi:  https://doi.org/10.1016/j.jtrangeo.2016.12.004 CrossRefGoogle Scholar
  16. Kim D, Ahn Y, Choi S et al., 2016. Sustainable mobility: longitudinal analysis of built environment on transit ridership. Sustainability, 8(10): 1016. doi:  https://doi.org/10.3390/su8101016 CrossRefGoogle Scholar
  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:  https://doi.org/10.1016/j.tra.2003.10.006 Google Scholar
  18. Lloyd C, Shuttleworth I, 2005. Analysing commuting using local regression techniques: scale, sensitivity, and geographical patterning. Environment and Planning A: Economy and Space, 37(1): 81–103. doi:  https://doi.org/10.1068/a36116 CrossRefGoogle Scholar
  19. Loo B P Y, Chen C, Chan ET H, 2010. Rail-based transit-oriented development: lessons from New York City and Hong Kong. Landscape and Urban Planning, 97(3): 202–212. doi:  https://doi.org/10.1016/j.landurbplan.2010.06.002 CrossRefGoogle Scholar
  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:  https://doi.org/10.1016/j.socscimed.2011.04.018 CrossRefGoogle Scholar
  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.Google Scholar
  22. McNally M G, 2007. The four-step model. In: Hensher D A, Button K J (eds). Handbook of Transport Modelling. Oxford: Pergamon, 35–53.CrossRefGoogle Scholar
  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)Google Scholar
  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:  https://doi.org/10.1016/j.jtrangeo.2005.11.002 CrossRefGoogle Scholar
  25. Pavlyuk D, 2016. Implication of spatial heterogeneity for airports’ efficiency estimation. Research in Transportation Economics, 56: 15–24. doi:  https://doi.org/10.1016/j.retrec.2016.07.002 CrossRefGoogle Scholar
  26. Pulugurtha S S, Agurla M, 2012. Assessment of models to estimate bus-stop level transit ridership using spatial modeling methods. Journal of Public Transportation, 15(1): 33–52. doi:  https://doi.org/10.5038/2375-0901.15.1.3 CrossRefGoogle Scholar
  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:  https://doi.org/10.1080/12265934.2013.835118 CrossRefGoogle Scholar
  28. Ryan S, Frank L F, 2009. Pedestrian environments and transit ridership. Journal of Public Transportation, 12(1): 39–57. doi:  https://doi.org/10.5038/2375-0901.12.1.3 CrossRefGoogle Scholar
  29. Sohn K, Shim H, 2010. Factors generating boardings at metro stations in the Seoul metropolitan area. Cities, 27(5): 358–368. doi:  https://doi.org/10.1016/j.cities.2010.05.001 CrossRefGoogle Scholar
  30. Sung H, Oh J T, 2011. Transit-oriented development in a high-density city: identifying its association with transit ridership in Seoul, Korea. Cities, 28(1): 70–82. doi:  https://doi.org/10.1016/j.cities.2010.09.004 CrossRefGoogle Scholar
  31. Tobler W R, 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(S1): 234–240.CrossRefGoogle Scholar
  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:  https://doi.org/10.1016/j.jtrangeo.2014.08.021 CrossRefGoogle Scholar
  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:  https://doi.org/10.1016/j.cities.2013.07.002 CrossRefGoogle Scholar
  34. Zhao J B, Deng W, Song Y et al., 2014. Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models. Transportation, 41(1): 133–155. doi:  https://doi.org/10.1007/s11116-013-9492-3 CrossRefGoogle Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zuoxian Gan
    • 1
  • Tao Feng
    • 2
  • Min Yang
    • 1
    Email author
  • Harry Timmermans
    • 2
  • Jinyu Luo
    • 1
  1. 1.Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of TransportationSoutheast UniversityNanjingChina
  2. 2.Urban Planning GroupEindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations