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

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  • 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
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    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Ben-Akiva, Moshe & Morikawa, Takayuki, 2002. "Comparing ridership attraction of rail and bus," Transport Policy, Elsevier, vol. 9(2), pages 107-116, April.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. David Hensher & Zheng Li, 2012. "Erratum to: Ridership drivers of bus rapid transit systems," Transportation, Springer, vol. 39(6), pages 1223-1224, November.
    19. 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.
    20. Robert Hannay & Martin Wachs, 2007. "Factors influencing support for local transportation sales tax measures," Transportation, Springer, vol. 34(1), pages 17-35, January.
    21. 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.
    22. Litman, Todd, 2007. "Evaluating rail transit benefits: A comment," Transport Policy, Elsevier, vol. 14(1), pages 94-97, January.
    23. David Hensher & Zheng Li, 2012. "Ridership drivers of bus rapid transit systems," Transportation, Springer, vol. 39(6), pages 1209-1221, November.
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