IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v102y2022ics096669232200103x.html
   My bibliography  Save this article

Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership

Author

Listed:
  • Du, Qiang
  • Zhou, Yuqing
  • Huang, Youdan
  • Wang, Yalei
  • Bai, Libiao

Abstract

Identifying the determinants of metro ridership is essential for metro planning and passenger flow management. However, few studies to date have empirically examined how accessibility affects metro ridership and even fewer have emphasized the non-linear impacts from a spatiotemporal perspective. This study demarcates station areas via the network-distance method and precisely quantifies the accessibility of metro stations both internally and externally. This is combined with a gradient boosting regression trees (GBRT) model and a Shapley additive explanations (SHAP) model to understand the non-linear impacts of accessibility on metro ridership from a spatiotemporal perspective. The results show that accessibility indicators collectively contribute more than 60% of the predictive power for metro ridership at different times and the external accessibility has a greater impact on metro ridership than internal accessibility. Some indicators, such as the shortest path and population density show threshold effects on metro ridership. More importantly, the results demonstrate significant spatial heterogeneity in the effects of accessibility indicators on metro ridership and geographic trends generally from urban to suburban areas. The findings are expected to help planning departments and transit agencies improve the coordinated development of metro systems.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jotrge:v:102:y:2022:i:c:s096669232200103x
    DOI: 10.1016/j.jtrangeo.2022.103380
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096669232200103X
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2022.103380?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. 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).
    2. Ding, Chuan & Cao, Xinyu & Wang, Yunpeng, 2018. "Synergistic effects of the built environment and commuting programs on commute mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 104-118.
    3. Caset, Freke & Blainey, Simon & Derudder, Ben & Boussauw, Kobe & Witlox, Frank, 2020. "Integrating node-place and trip end models to explore drivers of rail ridership in Flanders, Belgium," Journal of Transport Geography, Elsevier, vol. 87(C).
    4. 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.
    5. 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.
    6. 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.
    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. Bree, Sarah & Fuller, Daniel & Diab, Ehab, 2020. "Access to transit? Validating local transit accessibility measures using transit ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 430-442.
    9. Ding, Chuan & Cao, Xinyu (Jason) & Næss, Petter, 2018. "Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 107-117.
    10. Adam Millard-Ball, 2021. "Levine, Grengs, and Merlin: From Mobility to Accessibility: Transforming Urban Transportation and Land-Use Planning," Journal of the American Planning Association, Taylor & Francis Journals, vol. 87(1), pages 141-142, January.
    11. Li, Linbo & Ren, Huan & Zhao, Shanshan & Duan, Zhengyu & Zhang, Yahua & Zhang, Anming, 2017. "Two dimensional accessibility analysis of metro stations in Xi’an, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 414-426.
    12. 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.
    13. Tao, Tao & Wang, Jueyu & Cao, Xinyu, 2020. "Exploring the non-linear associations between spatial attributes and walking distance to transit," Journal of Transport Geography, Elsevier, vol. 82(C).
    14. Pfiester, Laura Mali & Thompson, Russell G. & Zhang, Lele, 2021. "Spatiotemporal exploration of Melbourne pedestrian demand," Journal of Transport Geography, Elsevier, vol. 95(C).
    15. 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.
    16. Shi, Yuji & Blainey, Simon & Sun, Chao & Jing, Peng, 2020. "A literature review on accessibility using bibliometric analysis techniques," Journal of Transport Geography, Elsevier, vol. 87(C).
    17. 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).
    18. 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.
    19. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    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.
    21. Zhengdong Huang & Ming Zhang & Xuejun Liu, 2017. "Estimating light-rail transit peak-hour boarding based on accessibility at station and route levels in Wuhan, China," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(5), pages 624-639, July.
    22. 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).
    23. Sung, Hyungun & Choi, Keechoo & Lee, Sugie & Cheon, SangHyun, 2014. "Exploring the impacts of land use by service coverage and station-level accessibility on rail transit ridership," Journal of Transport Geography, Elsevier, vol. 36(C), pages 134-140.
    24. 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.
    25. 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).
    26. 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.
    27. Wang, Chih-Hao & Chen, Na, 2017. "A geographically weighted regression approach to investigating the spatially varied built-environment effects on community opportunity," Journal of Transport Geography, Elsevier, vol. 62(C), pages 136-147.
    28. 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.
    29. 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.
    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, Rui & Derudder, Ben & Yang, Yongchun & Witlox, Frank, 2023. "The association between transit accessibility and space-time flexibility of shopping travel: On the moderating role of ICT use," Journal of Transport Geography, Elsevier, vol. 111(C).
    2. Zhenbao Wang & Shihao Li & Yushuo Zhang & Xiao Wang & Shuyue Liu & Dong Liu, 2024. "Built Environment Renewal Strategies Aimed at Improving Metro Station Vitality via the Interpretable Machine Learning Method: A Case Study of Beijing," Sustainability, MDPI, vol. 16(3), pages 1-26, January.
    3. Hong Zhu & Jin Li & Zhenjie Yuan & Jie Li, 2023. "Bibliometric Analysis of Spatial Accessibility from 1999–2022," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    4. Gao, Kun & Yang, Ying & Gil, Jorge & Qu, Xiaobo, 2023. "Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility," Journal of Transport Geography, Elsevier, vol. 110(C).
    5. 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).
    6. Lan Wu & Xiaorui Yuan & Chaoyin Yin & Ming Yang & Hongjian Ouyang, 2023. "Car Ownership Behavior Model Considering Nonlinear Impacts of Multi-Scale Built Environment Characteristics," Sustainability, MDPI, vol. 15(12), pages 1-14, June.

    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. 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. 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).
    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. 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.
    5. 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).
    6. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    7. 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).
    8. 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.
    9. 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).
    10. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    11. Zhang, Xiaojian & Zhao, Xilei, 2022. "Machine learning approach for spatial modeling of ridesourcing demand," Journal of Transport Geography, Elsevier, vol. 100(C).
    12. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    13. Yang, Jiawen & Cao, Jason & Zhou, Yufei, 2021. "Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 74-88.
    14. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
    15. 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).
    16. Caigang, Zhuang & Shaoying, Li & Zhangzhi, Tan & Feng, Gao & Zhifeng, Wu, 2022. "Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level," Journal of Transport Geography, Elsevier, vol. 102(C).
    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. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    19. 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.
    20. 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.

    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:102:y:2022:i:c:s096669232200103x. 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.