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Bus ridership and its determinants in Beijing: A spatial econometric perspective

Author

Listed:
  • Jiaoe Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yanan Li

    (Beijing Jiaotong University)

  • Jingjuan Jiao

    (Beijing Jiaotong University)

  • Haitao Jin

    (Beijing Information Science and Technology University)

  • Fangye Du

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Understanding the temporal and spatial dynamics and determinants of public transport ridership play an important role in urban planning. Previous studies have focused on exploring the determinants at the station level using global models, or a local model, geographically weighted regression (GWR), which cannot reveal spatial autocorrelation at the global level. This study explores the factors affecting bus ridership considering spatial autocorrelation using the spatial Durbin model (SDM). Taking the community in Beijing as the basic study unit, this study aims to explore the temporal and spatial dynamics of bus ridership and identify its key determinants considering neighboring effects. The results show the following: (1) The temporal dynamics are quite distinct on weekdays and weekends as well as at different time slots of the day. (2) The spatial patterns of bus ridership varied across different time slots, and the hot areas are mainly located near the central business district (CBD), transport hubs, and residential areas. (3) Key determinants of bus ridership varied across weekends and weekdays and varied at different time slots per day. (4) The spatial neighboring effects had been verified. This study provides a common analytical framework for analyzing the spatiotemporal dynamics and determinants of bus ridership at the community level.

Suggested Citation

  • Jiaoe Wang & Yanan Li & Jingjuan Jiao & Haitao Jin & Fangye Du, 2023. "Bus ridership and its determinants in Beijing: A spatial econometric perspective," Transportation, Springer, vol. 50(2), pages 383-406, April.
  • Handle: RePEc:kap:transp:v:50:y:2023:i:2:d:10.1007_s11116-021-10248-7
    DOI: 10.1007/s11116-021-10248-7
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    References listed on IDEAS

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    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. De Gruyter, Chris & Truong, Long T. & Taylor, Elizabeth J., 2020. "Can high quality public transport support reduced car parking requirements for new residential apartments?," Journal of Transport Geography, Elsevier, vol. 82(C).
    4. Chakour, Vincent & Eluru, Naveen, 2016. "Examining the influence of stop level infrastructure and built environment on bus ridership in Montreal," Journal of Transport Geography, Elsevier, vol. 51(C), pages 205-217.
    5. Buehler, Ralph, 2011. "Determinants of transport mode choice: a comparison of Germany and the USA," Journal of Transport Geography, Elsevier, vol. 19(4), pages 644-657.
    6. Jiao, Jingjuan & Wang, Jiaoe & Jin, Fengjun, 2017. "Impacts of high-speed rail lines on the city network in China," Journal of Transport Geography, Elsevier, vol. 60(C), pages 257-266.
    7. 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.
    8. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    9. Currie, Graham & Delbosc, Alexa, 2011. "Understanding bus rapid transit route ridership drivers: An empirical study of Australian BRT systems," Transport Policy, Elsevier, vol. 18(5), pages 755-764, September.
    10. Rahman, Syed & Balijepalli, Chandra, 2016. "Understanding the determinants of demand for public transport: Evidence from suburban rail operations in five divisions of Indian Railways," Transport Policy, Elsevier, vol. 48(C), pages 13-22.
    11. Bottasso, Anna & Conti, Maurizio & Ferrari, Claudio & Tei, Alessio, 2014. "Ports and regional development: A spatial analysis on a panel of European regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 44-55.
    12. Jie Huang & David Levinson & Jiaoe Wang & Haitao Jin, 2019. "Job-worker spatial dynamics in Beijing: Insights from Smart Card Data," Working Papers 2019-01, University of Minnesota: Nexus Research Group.
    13. 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.
    14. Jiao, Jingjuan & Wang, Jiaoe & Zhang, Fangni & Jin, Fengjun & Liu, Wei, 2020. "Roles of accessibility, connectivity and spatial interdependence in realizing the economic impact of high-speed rail: Evidence from China," Transport Policy, Elsevier, vol. 91(C), pages 1-15.
    15. 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.
    16. Jiaoe Wang & Jie Huang & Fangye Du, 2020. "Estimating spatial patterns of commute mode preference in Beijing," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 7(1), pages 382-386, January.
    17. Wang, Fahui & Antipova, Anzhelika & Porta, Sergio, 2011. "Street centrality and land use intensity in Baton Rouge, Louisiana," Journal of Transport Geography, Elsevier, vol. 19(2), pages 285-293.
    18. Boame, Attah K., 2004. "The technical efficiency of Canadian urban transit systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(5), pages 401-416, September.
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