IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v41y2014i1p133-155.html
   My bibliography  Save this article

Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models

Author

Listed:
  • Jinbao Zhao
  • Wei Deng
  • Yan Song
  • Yueran Zhu

Abstract

A growing base of research adopts direct demand models to reveal associations between transit ridership and influence factors in recent years. This study is designed to investigate the factors affecting rail transit ridership at both station level and station-to-station level by adopting multiple regression model and multiplicative model respectively, specifically using an implemented Metro system in Nanjing, China, where Metro implementation is on the rise. Independent variables include factors measuring land-use mix, intermodal connection, station context, and travel impedance. Multiple regression model proves 11 variables are significantly associated with Metro ridership at station level: population, employment, business/office floor area, CBD dummy variable, number of major educational sites, entertainment venues and shopping centers, road length, feeder bus lines, bicycle park-and-ride (P&R) spaces, and transfer dummy variable. Results from multiplicative model indicate that factors influencing Metro station ridership may also influence Metro station-to-station ridership, varied by both trip ends (origin/destination) and time of day. In comparison with previous case studies, CBD dummy variable and bicycle P&R are statistically significant to explain Metro ridership in Nanjing. In addition, Metro travel impedance variables have significant influence on station-to-station ridership, representing the basic time-decay relationship in travel distribution. Potential implications of the model results include estimating Metro ridership at station level and station-to-station level by considering the significant variables, recognizing the necessity to establish a cooperative multi-modal transit system, and identifying opportunities for transit-oriented development. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Jinbao Zhao & Wei Deng & Yan Song & Yueran Zhu, 2014. "Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models," Transportation, Springer, vol. 41(1), pages 133-155, January.
    • Handle: RePEc:kap:transp:v:41:y:2014:i:1:p:133-155
    DOI: 10.1007/s11116-013-9492-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11116-013-9492-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11116-013-9492-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gutiérrez, Javier & Cardozo, Osvaldo Daniel & García-Palomares, Juan Carlos, 2011. "Transit ridership forecasting at station level: an approach based on distance-decay weighted regression," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1081-1092.
    2. Kuby, Michael & Barranda, Anthony & Upchurch, Christopher, 2004. "Factors influencing light-rail station boardings in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 223-247, March.
    3. Guerra, Erick & Cervero, Robert & Tischler, Daniel, 2011. "The Half-Mile Circle: Does It Represent Transit Station Catchments?," University of California Transportation Center, Working Papers qt0d84c2f4, University of California Transportation Center.
    4. Guerra, Erick & Cervero, Robert & Tischler, Daniel, 2011. "The Half-Mile Circle: Does It Best Represent Transit Station Catchments?," University of California Transportation Center, Working Papers qt9jd6r1t9, University of California Transportation Center.
    5. Becky Loo & Dennis Li, 2006. "Developing Metro Systems in the People’s Republic of China: Policy and Gaps," Transportation, Springer, vol. 33(2), pages 115-132, March.
    6. Cervero, Robert & Murakami, Jin, 2008. "Rail + Property Development: A model of sustainable transit finance and urbanism," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6jx3k35x, Institute of Transportation Studies, UC Berkeley.
    7. Chiang, Wen-Chyuan & Russell, Robert A. & Urban, Timothy L., 2011. "Forecasting ridership for a metropolitan transit authority," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 696-705, August.
    8. Guerra, Erick & Cervero, Robert & Tischler, Daniel, 2011. "The Half-Mile Circle: Does It Best Represent Transit Station Catchments?," University of California Transportation Center, Working Papers qt68r764df, University of California Transportation Center.
    9. Baum-Snow, Nathaniel & Kahn, Matthew E., 2000. "The effects of new public projects to expand urban rail transit," Journal of Public Economics, Elsevier, vol. 77(2), pages 241-263, August.
    10. David Hensher & Zheng Li, 2012. "Erratum to: Ridership drivers of bus rapid transit systems," Transportation, Springer, vol. 39(6), pages 1223-1224, November.
    11. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    12. Litman, Todd, 2007. "Evaluating rail transit benefits: A comment," Transport Policy, Elsevier, vol. 14(1), pages 94-97, January.
    13. Yoram Shiftan & John Suhrbier, 2002. "The analysis of travel and emission impacts of travel demand management strategies using activity-based models," Transportation, Springer, vol. 29(2), pages 145-168, May.
    14. Coombes, Emma & Jones, Andrew P. & Hillsdon, Melvyn, 2010. "The relationship of physical activity and overweight to objectively measured green space accessibility and use," Social Science & Medicine, Elsevier, vol. 70(6), pages 816-822, March.
    15. Daniel Hess, 2012. "Walking to the bus: perceived versus actual walking distance to bus stops for older adults," Transportation, Springer, vol. 39(2), pages 247-266, March.
    16. Ben-Akiva, Moshe & Morikawa, Takayuki, 2002. "Comparing ridership attraction of rail and bus," Transport Policy, Elsevier, vol. 9(2), pages 107-116, April.
    17. Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
    18. Olaru, Doina & Smith, Brett & Taplin, John H.E., 2011. "Residential location and transit-oriented development in a new rail corridor," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(3), pages 219-237, March.
    19. Mamun, Sha A. & Lownes, Nicholas E. & Osleeb, Jeffrey P. & Bertolaccini, Kelly, 2013. "A method to define public transit opportunity space," Journal of Transport Geography, Elsevier, vol. 28(C), pages 144-154.
    20. Cervero, Robert & Murakami, Jin & Miller, Mark A., 2009. "Direct Ridership Model of Bus Rapid Transit in Los Angeles County," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt39q7w812, Institute of Transportation Studies, UC Berkeley.
    21. Robert Hannay & Martin Wachs, 2007. "Factors influencing support for local transportation sales tax measures," Transportation, Springer, vol. 34(1), pages 17-35, January.
    22. Kevin Manaugh & Luis Miranda-Moreno & Ahmed El-Geneidy, 2010. "The effect of neighbourhood characteristics, accessibility, home–work location, and demographics on commuting distances," Transportation, Springer, vol. 37(4), pages 627-646, July.
    23. David Hensher & Zheng Li, 2012. "Ridership drivers of bus rapid transit systems," Transportation, Springer, vol. 39(6), pages 1209-1221, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    2. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Teller, David, 2020. "Study design impacts on built environment and transit use research," Journal of Transport Geography, Elsevier, vol. 82(C).
    3. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(C).
    4. Kepaptsoglou, Konstantinos & Stathopoulos, Antony & Karlaftis, Matthew G., 2017. "Ridership estimation of a new LRT system: Direct demand model approach," Journal of Transport Geography, Elsevier, vol. 58(C), pages 146-156.
    5. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    6. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    7. Wang, Jing & Wan, Feng & Dong, Chunjiao & Yin, Chaoying & Chen, Xiaoyu, 2023. "Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns," Journal of Transport Geography, Elsevier, vol. 109(C).
    8. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    9. Zhenbao Wang & Jiarui Song & Yuchen Zhang & Shihao Li & Jianlin Jia & Chengcheng Song, 2022. "Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    10. Lu, Qing-Chang & Li, Jing & Xu, Peng-Cheng & Zhang, Lei & Cui, Xin, 2024. "Modeling cascading failures of urban rail transit network based on passenger spatiotemporal heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    11. Iseki, Hiroyuki & Liu, Chao & Knaap, Gerrit, 2018. "The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 635-649.
    12. Xin Tong & Yaowu Wang & Edwin H. W. Chan & Qingfeng Zhou, 2018. "Correlation between Transit-Oriented Development (TOD), Land Use Catchment Areas, and Local Environmental Transformation," Sustainability, MDPI, vol. 10(12), pages 1-21, December.
    13. Chuan Ding & Donggen Wang & Xiaolei Ma & Haiying Li, 2016. "Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees," Sustainability, MDPI, vol. 8(11), pages 1-16, October.
    14. Wei Yu & Hua Bai & Jun Chen & Xingchen Yan, 2019. "Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro," Sustainability, MDPI, vol. 11(18), pages 1-19, September.
    15. Zhuangbin Shi & Ning Zhang & Yang Liu & Wei Xu, 2018. "Exploring Spatiotemporal Variation in Hourly Metro Ridership at Station Level: The Influence of Built Environment and Topological Structure," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    16. Xia Li & Zhenyu Liu & Xinwei Ma, 2022. "Measuring Access and Egress Distance and Catchment Area of Multiple Feeding Modes for Metro Transferring Using Survey Data," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    17. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    18. Gao, Fan & Yang, Linchuan & Han, Chunyang & Tang, Jinjun & Li, Zhitao, 2022. "A network-distance-based geographically weighted regression model to examine spatiotemporal effects of station-level built environments on metro ridership," Journal of Transport Geography, Elsevier, vol. 105(C).
    19. Suchi Kapoor Malhotra & Howard White & Nina Ashley O. Dela Cruz & Ashrita Saran & John Eyers & Denny John & Ella Beveridge & Nina Blöndal, 2021. "Studies of the effectiveness of transport sector interventions in low‐ and middle‐income countries: An evidence and gap map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(4), December.
    20. Park, Chung & Lee, Jungpyo & Sohn, So Young, 2019. "Recommendation of feeder bus routes using neural network embedding-based optimization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 329-341.
    21. Lijie Yu & Yarong Cong & Kuanmin Chen, 2020. "Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression," Sustainability, MDPI, vol. 12(6), pages 1-22, March.
    22. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Teller, David, 2020. "Study design impacts on built environment and transit use research," Journal of Transport Geography, Elsevier, vol. 82(C).
    2. Kepaptsoglou, Konstantinos & Stathopoulos, Antony & Karlaftis, Matthew G., 2017. "Ridership estimation of a new LRT system: Direct demand model approach," Journal of Transport Geography, Elsevier, vol. 58(C), pages 146-156.
    3. Vergel-Tovar, C. Erik & Rodriguez, Daniel A., 2018. "The ridership performance of the built environment for BRT systems: Evidence from Latin America," Journal of Transport Geography, Elsevier, vol. 73(C), pages 172-184.
    4. Ingvardson, Jesper Bláfoss & Nielsen, Otto Anker, 2018. "How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas," Journal of Transport Geography, Elsevier, vol. 72(C), pages 50-63.
    5. Dohyung Kim & Yongjin Ahn & Simon Choi & Kwangkoo Kim, 2016. "Sustainable Mobility: Longitudinal Analysis of Built Environment on Transit Ridership," Sustainability, MDPI, vol. 8(10), pages 1-14, October.
    6. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    7. Wasserman, Jacob L. & Taylor, Brian D., 2023. "State of the BART: Analyzing the Determinants of Bay Area Rapid Transit Use in the 2010s," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    8. Cervero, Robert & Guerra, Erick, 2011. "Urban Densities and Transit: A Multi-dimensional Perspective," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3mb598qr, Institute of Transportation Studies, UC Berkeley.
    9. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    10. Vale, David S. & Viana, Cláudia M. & Pereira, Mauro, 2018. "The extended node-place model at the local scale: Evaluating the integration of land use and transport for Lisbon's subway network," Journal of Transport Geography, Elsevier, vol. 69(C), pages 282-293.
    11. Diab, Ehab & Kasraian, Dena & Miller, Eric J. & Shalaby, Amer, 2020. "The rise and fall of transit ridership across Canada: Understanding the determinants," Transport Policy, Elsevier, vol. 96(C), pages 101-112.
    12. Jyothi Chava & Peter Newman, 2016. "Stakeholder Deliberation on Developing Affordable Housing Strategies: Towards Inclusive and Sustainable Transit-Oriented Developments," Sustainability, MDPI, vol. 8(10), pages 1-21, October.
    13. Heilmann, Kilian, 2018. "Transit access and neighborhood segregation. Evidence from the Dallas light rail system," Regional Science and Urban Economics, Elsevier, vol. 73(C), pages 237-250.
    14. Aston, Laura & Currie, Graham & Pavkova, Katerina, 2016. "Does transit mode influence the transit-orientation of urban development? – An empirical study," Journal of Transport Geography, Elsevier, vol. 55(C), pages 83-91.
    15. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    16. Yuxin He & Yang Zhao & Kwok Leung Tsui, 2021. "An adapted geographically weighted LASSO (Ada-GWL) model for predicting subway ridership," Transportation, Springer, vol. 48(3), pages 1185-1216, June.
    17. Iseki, Hiroyuki & Liu, Chao & Knaap, Gerrit, 2018. "The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 635-649.
    18. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    19. Mishra, Sabyasachee & Welch, Timothy F. & Jha, Manoj K., 2012. "Performance indicators for public transit connectivity in multi-modal transportation networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(7), pages 1066-1085.
    20. Singh, Yamini Jain & Lukman, Azhari & Flacke, Johannes & Zuidgeest, Mark & Van Maarseveen, M.F.A.M., 2017. "Measuring TOD around transit nodes - Towards TOD policy," Transport Policy, Elsevier, vol. 56(C), pages 96-111.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:41:y:2014:i:1:p:133-155. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.