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Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations

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  • Wu, Pan
  • Xu, Lunhui
  • Zhong, Lingshu
  • Gao, Kun
  • Qu, Xiaobo
  • Pei, Mingyang

Abstract

Buses and metros are two main public transit modes, and these modes are crucial components of sustainable transportation systems. Promoting reciprocal integration between bus and metro systems requires a deep understanding of the effects of multiple factors on transfers among integrated public transportation transfer modes, i.e., metro-to-bus and bus-to-metro. This study aims to reveal the determinants of the transfer ratio between bus and metro systems and quantify the associated impacts. The transfer ratio between buses and metros is identified based on large-scale transaction data from automated fare collection systems. Meanwhile, various influencing factors, including weather, socioeconomic, the intensity of business activities, and built environment factors, are obtained from multivariate sources. A multivariate regression model is used to investigate the associations between the transfer ratio and multiple factors. The results show that the transfer ratio of the two modes significantly increases under high temperature, strong wind, rainfall, and low visibility. The morning peak hours attract a transfer ratio of up to 57.95%, and the average hourly transfer volume is 0.94 to 1.38 times higher at this time than in other periods. The intensity of business activities has the most significant impact on the transfer ratio, which is approximately 1.5 to 15 times that of the other independent variables. Moreover, an adaptative geographically weighted regression is utilized to investigate the spatial divergences of the influences of critical factors on the transfer ratio. The results indicate that the impact of a factor presents spatial heterogeneity and even shows opposite effects (in terms of positive and negative) on the transfer ratio in different urban contexts. For example, among the related socioeconomic variables, the impact of the housing price on the downtown transfer ratio is larger than that in the suburbs. Crowd density positively influences the transfer ratio at most stations in the northern region, whereas it shows negative results in the southern region. These findings provide valuable insights for public transportation management and promote the effective integration of bus and metro systems to provide enhanced transfer services.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jotrge:v:104:y:2022:i:c:s0966692322001387
    DOI: 10.1016/j.jtrangeo.2022.103415
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    as
    1. 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.
    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. 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.
    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. 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).
    6. 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.
    7. Jinkyung Choi & Yong Lee & Taewan Kim & Keemin Sohn, 2012. "An analysis of Metro ridership at the station-to-station level in Seoul," Transportation, Springer, vol. 39(3), pages 705-722, May.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Hadas, Yuval & Ranjitkar, Prakash, 2012. "Modeling public-transit connectivity with spatial quality-of-transfer measurements," Journal of Transport Geography, Elsevier, vol. 22(C), pages 137-147.
    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. 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.
    17. Schakenbos, Rik & Paix, Lissy La & Nijenstein, Sandra & Geurs, Karst T., 2016. "Valuation of a transfer in a multimodal public transport trip," Transport Policy, Elsevier, vol. 46(C), pages 72-81.
    18. 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.
    19. 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.
    20. 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.
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