IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9666-d881412.html
   My bibliography  Save this article

How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

Author

Listed:
  • Pan Wu

    (Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China)

  • Jinlong Li

    (Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China)

  • Yuzhuang Pian

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Xiaochen Li

    (Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China)

  • Zilin Huang

    (Center for Connected and Automated Transportation (CCAT), Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA)

  • Lunhui Xu

    (Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China)

  • Guilin Li

    (Chongqing Dajiang-Jiexin Forging Co., Ltd., Chongqing 401321, China)

  • Ruonan Li

    (School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China)

Abstract

Understanding the determinants of transfer ridership is important for providing insights into improving the attractiveness of transit systems and building reliable and resilient metro stations. This study focuses on the transfer ridership between bus and metro systems under different dates and severe weather conditions to quantify the impacts of various attributes on the transfer ridership of different transfer modes (metro-to-bus and bus-to-metro). A multivariate generalized Poisson regression (GPR) model is applied to investigate the effects of critical factors on the transfer ridership of different transfer modes on weekdays, holidays, and typhoon days, respectively. The results indicate that the transfer-related variables, real-time weather, socioeconomic characteristics, and built environment significantly affect the transfer ridership. Concretely, the influence of socioeconomic and demographic factors on transfer ridership is the most significant on different types of dates, which is approximately 1.19 to 9.28 times that of the other variables. Weather variables have little effect on transfer ridership on weekdays, but they have a more significant impact on the transfer ridership on holidays and typhoon days. Specifically, during typhoons, transfer ridership is more affected by the weather factors: the coefficients are about 2.36 to 4.74 times higher than that in the other periods. Moreover, under strong wind speed, heavy rain, and high-temperature conditions, transfer ridership of the metro-to-bus mode significantly increases. In contrast, transfer ridership of the bus-to-metro mode rapidly decreases. Additionally, the peak hours have a strong positive influence on the transfer ridership, and the average hourly transfer ridership during peak hours is 1.16 to 4.02 times higher than that during the other periods. These findings indicate that the effect of each factor on transfer ridership varies with dates and transfer modes. This can also provide support for improving metro stations and increasing the attractiveness of public transport.

Suggested Citation

  • Pan Wu & Jinlong Li & Yuzhuang Pian & Xiaochen Li & Zilin Huang & Lunhui Xu & Guilin Li & Ruonan Li, 2022. "How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method," Sustainability, MDPI, vol. 14(15), pages 1-31, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9666-:d:881412
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9666/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/15/9666/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Böcker, Lars & Dijst, Martin & Faber, Jan, 2016. "Weather, transport mode choices and emotional travel experiences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 360-373.
    2. Allard, Ryan F. & Moura, Filipe, 2018. "Effect of transport transfer quality on intercity passenger mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 89-107.
    3. Navarrete, Francisca Javiera & Ortúzar, Juan de Dios, 2013. "Subjective valuation of the transit transfer experience: The case of Santiago de Chile," Transport Policy, Elsevier, vol. 25(C), pages 138-147.
    4. Müller, Sven & Tscharaktschiew, Stefan & Haase, Knut, 2008. "Travel-to-school mode choice modelling and patterns of school choice in urban areas," Journal of Transport Geography, Elsevier, vol. 16(5), pages 342-357.
    5. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    6. Xinghua Hu & Yimei Xu & Jianpu Guo & Tingting Zhang & Yuhang Bi & Wei Liu & Xiaochuan Zhou, 2022. "A Complete Information Interaction-Based Bus Passenger Flow Control Model for Epidemic Spread Prevention," Sustainability, MDPI, vol. 14(13), pages 1-11, June.
    7. Krygsman, Stephan & Dijst, Martin & Arentze, Theo, 2004. "Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio," Transport Policy, Elsevier, vol. 11(3), pages 265-275, July.
    8. Garcia-Martinez, Andres & Cascajo, Rocio & Jara-Diaz, Sergio R. & Chowdhury, Subeh & Monzon, Andres, 2018. "Transfer penalties in multimodal public transport networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PA), pages 52-66.
    9. Shuya Zong & Sikai Chen & Majed Alinizzi & Samuel Labi, 2022. "Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    10. Chen, Xi & Yu, Ruyi & Ullah, Sajid & Wu, Dianming & Li, Zhiqiang & Li, Qingli & Qi, Honggang & Liu, Jihui & Liu, Min & Zhang, Yundong, 2022. "A novel loss function of deep learning in wind speed forecasting," Energy, Elsevier, vol. 238(PB).
    11. Gao, Kun & Yang, Ying & Li, Aoyong & Li, Junhong & Yu, Bo, 2021. "Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 89-103.
    12. Fu Wang & Manqing Ye & Hongbin Zhu & Dengjun Gu, 2022. "Optimization Method for Conventional Bus Stop Placement and the Bus Line Network Based on the Voronoi Diagram," Sustainability, MDPI, vol. 14(13), pages 1-20, June.
    13. Fan Wu & Wei Ma, 2022. "Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    14. Yang, Xiaobao & Yue, Xianfei & Sun, Huijun & Gao, Ziyou & Wang, Wencheng, 2021. "Impact of weather on freeway origin-destination volume in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 143(C), pages 30-47.
    15. Espino, Raquel & Román, Concepción, 2020. "Valuation of transfer for bus users: The case of Gran Canaria," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 131-144.
    16. 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).
    17. Miao, Qing & Welch, Eric W. & Sriraj, P.S., 2019. "Extreme weather, public transport ridership and moderating effect of bus stop shelters," Journal of Transport Geography, Elsevier, vol. 74(C), pages 125-133.
    18. Mutz, Rüdiger & Daniel, Hans-Dieter, 2019. "How to consider fractional counting and field normalization in the statistical modeling of bibliometric data: A multilevel Poisson regression approach," Journal of Informetrics, Elsevier, vol. 13(2), pages 643-657.
    19. Lars Böcker & Martin Dijst & Jan Prillwitz, 2013. "Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective: A Literature Review," Transport Reviews, Taylor & Francis Journals, vol. 33(1), pages 71-91, January.
    20. Wei, Ming & Liu, Yan & Sigler, Thomas & Liu, Xiaoyang & Corcoran, Jonathan, 2019. "The influence of weather conditions on adult transit ridership in the sub-tropics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 106-118.
    21. Pineda, Cristobal & Schwarz, Daniel & Godoy, Esteban, 2016. "Comparison of passengers' behavior and aggregate demand levels on a subway system using origin-destination surveys and smartcard data," Research in Transportation Economics, Elsevier, vol. 59(C), pages 258-267.
    22. Bae, S. & Famoye, F. & Wulu, J.T. & Bartolucci, A.A. & Singh, K.P., 2005. "A rich family of generalized Poisson regression models with applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 69(1), pages 4-11.
    23. Singhal, Abhishek & Kamga, Camille & Yazici, Anil, 2014. "Impact of weather on urban transit ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 379-391.
    24. Muhammad Fadhlullah Abu Bakar & Shuhairy Norhisham & Herda Yati Katman & Chow Ming Fai & Nor Najwa Irina Mohd Azlan & Nur Sarah Shaziah Samsudin, 2022. "Service Quality of Bus Performance in Asia: A Systematic Literature Review and Conceptual Framework," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    25. Arana, P. & Cabezudo, S. & Peñalba, M., 2014. "Influence of weather conditions on transit ridership: A statistical study using data from Smartcards," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 1-12.
    26. De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
    27. De Zhao & Wei Wang & Chenyang Li & Yanjie Ji & Xiaojian Hu & Wenfu Wang, 2019. "Recognizing metro-bus transfers from smart card data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(1), pages 70-83, January.
    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. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.

    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. Wu, Pan & Xu, Lunhui & Zhong, Lingshu & Gao, Kun & Qu, Xiaobo & Pei, Mingyang, 2022. "Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations," Journal of Transport Geography, Elsevier, vol. 104(C).
    2. Yang, Xiaobao & Yue, Xianfei & Sun, Huijun & Gao, Ziyou & Wang, Wencheng, 2021. "Impact of weather on freeway origin-destination volume in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 143(C), pages 30-47.
    3. Wei, Ming, 2022. "Investigating the influence of weather on public transit passenger’s travel behaviour: Empirical findings from Brisbane, Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 36-51.
    4. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    5. Yuan, Yalong & Yang, Min & Feng, Tao & Ma, Yafeng & Ren, Yifeng & Ruan, Xinpei, 2022. "Heterogeneity in the transfer time of air-rail intermodal passengers based on ticket booking data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 533-552.
    6. Kevin Lanza & Casey P. Durand, 2021. "Heat-Moderating Effects of Bus Stop Shelters and Tree Shade on Public Transport Ridership," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    7. Timothy Otim & Leandro Dörfer & Dina Bousdar Ahmed & Estefania Munoz Diaz, 2022. "Modeling the Impact of Weather and Context Data on Transport Mode Choices: A Case Study of GPS Trajectories from Beijing," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    8. Wu, Jingwen & Liao, Hua, 2020. "Weather, travel mode choice, and impacts on subway ridership in Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 264-279.
    9. Wei, Ming, 2022. "How does the weather affect public transit ridership? A model with weather-passenger variations," Journal of Transport Geography, Elsevier, vol. 98(C).
    10. Chen, Enhui & Stathopoulos, Amanda & Nie, Yu (Marco), 2022. "Transfer station choice in a multimodal transit system: An empirical study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 337-355.
    11. Wei, Ming & Liu, Yan & Sigler, Thomas & Liu, Xiaoyang & Corcoran, Jonathan, 2019. "The influence of weather conditions on adult transit ridership in the sub-tropics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 106-118.
    12. Miao, Qing & Welch, Eric W. & Sriraj, P.S., 2019. "Extreme weather, public transport ridership and moderating effect of bus stop shelters," Journal of Transport Geography, Elsevier, vol. 74(C), pages 125-133.
    13. Bean, Richard & Pojani, Dorina & Corcoran, Jonathan, 2021. "How does weather affect bikeshare use? A comparative analysis of forty cities across climate zones," Journal of Transport Geography, Elsevier, vol. 95(C).
    14. Markolf, Samuel A. & Hoehne, Christopher & Fraser, Andrew & Chester, Mikhail V. & Underwood, B. Shane, 2019. "Transportation resilience to climate change and extreme weather events – Beyond risk and robustness," Transport Policy, Elsevier, vol. 74(C), pages 174-186.
    15. Faber, R.M. & Jonkeren, O. & de Haas, M.C. & Molin, E.J.E. & Kroesen, M., 2022. "Inferring modality styles by revealing mode choice heterogeneity in response to weather conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 282-295.
    16. Zanni, Alberto M. & Ryley, Tim J., 2015. "The impact of extreme weather conditions on long distance travel behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 305-319.
    17. Yue Liu & Jun Chen & Weiguang Wu & Jiao Ye, 2019. "Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    18. An, Ran & Zahnow, Renee & Pojani, Dorina & Corcoran, Jonathan, 2019. "Weather and cycling in New York: The case of Citibike," Journal of Transport Geography, Elsevier, vol. 77(C), pages 97-112.
    19. Hong, Jinhyun & Philip McArthur, David & Stewart, Joanna L., 2020. "Can providing safe cycling infrastructure encourage people to cycle more when it rains? The use of crowdsourced cycling data (Strava)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 109-121.
    20. Romero, Fernando & Gomez, Juan & Paez, Antonio & Vassallo, José Manuel, 2020. "Toll roads vs. Public transportation: A study on the acceptance of congestion-calming measures in Madrid," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 319-342.

    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:15:p:9666-:d:881412. 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.