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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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
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