IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i5p2841-d761253.html
   My bibliography  Save this article

Measuring Access and Egress Distance and Catchment Area of Multiple Feeding Modes for Metro Transferring Using Survey Data

Author

Listed:
  • Xia Li

    (School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Zhenyu Liu

    (School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Xinwei Ma

    (School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China)

Abstract

Multiple feeding modes, including walking, bus, private bike, docked bike-sharing, private electric bike (e-bike), car, and taxi, are applied for better accessibility in a metro-based trip. It is crucial to understand their access/egress distances and corresponding catchment areas of metro stations. This paper determines these two distances and accessible areas of stations for different feeding modes based on Nanjing Population Survey data and GIS data by using a network-based approach in Nanjing, China. Considering the distribution of access/egress distance, regression models are established for the exploration of the threshold of distance to delineate catchment areas. What is more, the spatio-temporal characteristics of multiple feeding modes are analyzed. The results indicate that the average feeding distance of walking is the shortest, but docked bike-sharing has the shortest average feeding time, about 8 min. The average feeding time of private e-bikes is close to that of the private bike, but the feeding distance of private e-bikes is about 1.3 times as long as that of private bikes. Moreover, the origin of an over-10 km transfer for accessing metro stations is usually far away from metro lines and the transferring station is mostly the terminal station. Generally, longer access distance means larger catchment area but the result is also influenced by the condition of street network. Moreover, catchment areas for the same feeding modes are different between urban and suburban areas.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2841-:d:761253
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/5/2841/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/5/2841/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elliot Fishman & Christopher Cherry, 2016. "E-bikes in the Mainstream: Reviewing a Decade of Research," Transport Reviews, Taylor & Francis Journals, vol. 36(1), pages 72-91, January.
    2. John Parkin & Mark Wardman & Matthew Page, 2008. "Estimation of the determinants of bicycle mode share for the journey to work using census data," Transportation, Springer, vol. 35(1), pages 93-109, January.
    3. Givoni, Moshe & Rietveld, Piet, 2007. "The access journey to the railway station and its role in passengers' satisfaction with rail travel," Transport Policy, Elsevier, vol. 14(5), pages 357-365, September.
    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. 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.
    6. Weiss, Adam & Habib, Khandker Nurul, 2017. "Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model," Journal of Transport Geography, Elsevier, vol. 59(C), pages 111-119.
    7. Miaoyi Li & Lei Dong & Zhenjiang Shen & Wei Lang & Xinyue Ye, 2017. "Examining the Interaction of Taxi and Subway Ridership for Sustainable Urbanization," Sustainability, MDPI, vol. 9(2), pages 1-12, February.
    8. Ahmed El-Geneidy & Michael Grimsrud & Rania Wasfi & Paul Tétreault & Julien Surprenant-Legault, 2014. "New evidence on walking distances to transit stops: identifying redundancies and gaps using variable service areas," Transportation, Springer, vol. 41(1), pages 193-210, January.
    9. Wang, Jueyu & Cao, Xinyu, 2017. "Exploring built environment correlates of walking distance of transit egress in the Twin Cities," Journal of Transport Geography, Elsevier, vol. 64(C), pages 132-138.
    10. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    11. Ma, Xinwei & Ji, Yanjie & Yang, Mingyuan & Jin, Yuchuan & Tan, Xu, 2018. "Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data," Transport Policy, Elsevier, vol. 71(C), pages 57-69.
    12. Sylvia Y. He & Genevieve Giuliano, 2017. "Factors affecting children’s journeys to school: a joint escort-mode choice model," Transportation, Springer, vol. 44(1), pages 199-224, January.
    13. Tirachini, Alejandro, 2014. "The economics and engineering of bus stops: Spacing, design and congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 37-57.
    14. Zhibin Li & Wei Wang & Chen Yang & Haoyang Ding, 2017. "Bicycle mode share in China: a city-level analysis of long term trends," Transportation, Springer, vol. 44(4), pages 773-788, July.
    15. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    16. Zuo, Ting & Wei, Heng & Rohne, Andrew, 2018. "Determining transit service coverage by non-motorized accessibility to transit: Case study of applying GPS data in Cincinnati metropolitan area," Journal of Transport Geography, Elsevier, vol. 67(C), pages 1-11.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. 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.
    3. 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.
    4. Xuesong Feng & Zhibin Tao & Xuejun Niu & Zejing Ruan, 2021. "Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    5. 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.
    6. Zuo, Ting & Wei, Heng, 2019. "Bikeway prioritization to increase bicycle network connectivity and bicycle-transit connection: A multi-criteria decision analysis approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 52-71.
    7. 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).
    8. 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.
    9. Pueboobpaphan, Rattaphol & Pueboobpaphan, Suthatip & Sukhotra, Suthasinee, 2022. "Acceptable walking distance to transit stations in Bangkok, Thailand: Application of a stated preference technique," Journal of Transport Geography, Elsevier, vol. 99(C).
    10. 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.
    11. Panyu Tang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    12. 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).
    13. Zhan, Zilin & Guo, Yuanyuan & Noland, Robert B. & He, Sylvia Y. & Wang, Yacan, 2023. "Analysis of links between dockless bikeshare and metro trips in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    14. Li, Wenxiang & Chen, Shawen & Dong, Jieshuang & Wu, Jingxian, 2021. "Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros," Journal of Transport Geography, Elsevier, vol. 92(C).
    15. Wu, Xueying & Lu, Yi & Gong, Yongxi & Kang, Yuhao & Yang, Linchuan & Gou, Zhonghua, 2021. "The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space," Journal of Transport Geography, Elsevier, vol. 97(C).
    16. 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).
    17. 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).
    18. Pucher, John & Buehler, Ralph & Seinen, Mark, 2011. "Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 451-475, July.
    19. 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.
    20. Manout, Ouassim & Bonnel, Patrick & Bouzouina, Louafi, 2018. "Transit accessibility: A new definition of transit connectors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 88-100.

    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:gam:jsusta:v:14:y:2022:i:5:p:2841-:d:761253. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.