IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v109y2023ics0966692323000698.html
   My bibliography  Save this article

Spatiotemporal effects of built environment factors on varying rail transit station ridership patterns

Author

Listed:
  • Wang, Jing
  • Wan, Feng
  • Dong, Chunjiao
  • Yin, Chaoying
  • Chen, Xiaoyu

Abstract

Although several studies have explored the impacts of built environment factors on rail transit station ridership, the spatiotemporal heterogeneity of the effects and different ridership patterns have rarely been considered. This study explored different ridership patterns and considered the spatiotemporal heterogeneity of the effects. Taking Beijing, China, as an example, the built environment factors were precisely characterized by integrating multi-source big data, including mobile phone, point of interest (POI), housing prices, and road network data. Three direct ridership models (DRMs) based on the geographically and temporally weighted regression (GTWR) model were proposed to reveal the spatiotemporal effects of built environment factors on daily, weekday hourly, and weekend hourly ridership patterns. The results showed that population density and employment density are the dominant factors affecting commuting ridership; however, the spatial variations of their effects exhibit significantly different distributions. Entertainment places are highly positively related to weekend ridership, and the strongest coefficients are mainly concentrated in the southwestern urban center and suburban residential areas. The results also highlighted the importance of bus stations and parking lots in attracting commuting ridership. These findings can provide insights into urban rail transit planning and facilitate the coordinated development of urban public transportation systems and land use.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jotrge:v:109:y:2023:i:c:s0966692323000698
    DOI: 10.1016/j.jtrangeo.2023.103597
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692323000698
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2023.103597?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Lahoorpoor, Bahman & Levinson, David M., 2020. "Catchment if you can: The effect of station entrance and exit locations on accessibility," Journal of Transport Geography, Elsevier, vol. 82(C).
    4. 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).
    5. Li, Xijing & Zhang, Mengmeng & Wang, Jionghua, 2022. "The spatio-temporal relationship between land use and population distribution around new intercity railway stations: A case study on the Pearl River Delta region, China," Journal of Transport Geography, Elsevier, vol. 98(C).
    6. 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.
    7. 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.
    8. Ma, Xiaolei & Liu, Congcong & Wen, Huimin & Wang, Yunpeng & Wu, Yao-Jan, 2017. "Understanding commuting patterns using transit smart card data," Journal of Transport Geography, Elsevier, vol. 58(C), pages 135-145.
    9. 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.
    10. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    11. 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.
    12. 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.
    13. 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.
    14. Merlin, Louis A. & Singer, Matan & Levine, Jonathan, 2021. "Influences on transit ridership and transit accessibility in US urban areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 63-73.
    15. Taylor, Brian D. & Miller, Douglas & Iseki, Hiroyuki & Fink, Camille, 2003. "Analyzing the Determinants of Transit Ridership Using a Two-Stage Least Squares Regression on a National Sample of Urbanized Areas," University of California Transportation Center, Working Papers qt7xf3q4vh, University of California Transportation Center.
    16. Ma, Xinwei & Ji, Yanjie & Yuan, Yufei & Van Oort, Niels & Jin, Yuchuan & Hoogendoorn, Serge, 2020. "A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 148-173.
    17. Zhao, Pengjun & Lü, Bin & Roo, Gert de, 2011. "Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era," Journal of Transport Geography, Elsevier, vol. 19(1), pages 59-69.
    18. Zhang, Dapeng & Wang, Xiaokun (Cara), 2014. "Transit ridership estimation with network Kriging: a case study of Second Avenue Subway, NYC," Journal of Transport Geography, Elsevier, vol. 41(C), pages 107-115.
    19. 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.
    20. Zhou, Xingang & Yeh, Anthony G.O. & Yue, Yang, 2018. "Spatial variation of self-containment and jobs-housing balance in Shenzhen using cellphone big data," Journal of Transport Geography, Elsevier, vol. 68(C), pages 102-108.
    21. Blainey, Simon, 2010. "Trip end models of local rail demand in England and Wales," Journal of Transport Geography, Elsevier, vol. 18(1), pages 153-165.
    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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. Gao, Fan & Han, Chunyang & Yang, Linchuan & Liang, Jian & He, Xuan & Li, Fan, 2024. "Analyzing spatiotemporal distribution patterns of metro ridership: Comparison between common-class and business-class carriage service," Journal of Transport Geography, Elsevier, vol. 115(C).
    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. 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.
    11. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    12. 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.
    13. 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.
    14. Liu, Xiang & Chen, Xiaohong & Tian, Mingshu & De Vos, Jonas, 2023. "Effects of buffer size on associations between the built environment and metro ridership: A machine learning-based sensitive analysis," Journal of Transport Geography, Elsevier, vol. 113(C).
    15. Yadi Zhu & Feng Chen & Zijia Wang & Jin Deng, 2019. "Spatio-temporal analysis of rail station ridership determinants in the built environment," Transportation, Springer, vol. 46(6), pages 2269-2289, December.
    16. Su, Shiliang & Zhao, Chong & Zhou, Hao & Li, Bozhao & Kang, Mengjun, 2022. "Unraveling the relative contribution of TOD structural factors to metro ridership: A novel localized modeling approach with implications on spatial planning," Journal of Transport Geography, Elsevier, vol. 100(C).
    17. 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.
    18. Wu, Hao & Lee, Jinwoo (Brian) & Levinson, David, 2023. "The node-place model, accessibility, and station level transit ridership," Journal of Transport Geography, Elsevier, vol. 113(C).
    19. 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.
    20. Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.

    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:eee:jotrge:v:109:y:2023:i:c:s0966692323000698. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.