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The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model

Author

Listed:
  • Yuan Yuan

    (Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China)

  • Chunfu Shao

    (Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China)

  • Zhichao Cao

    (School of Transportation and Civil Engineering, Nantong University, Nantong 226000, China)

  • Chaoying Yin

    (Key Laboratory of Integrated Transportation Big Data Application Technology in Transportation Industry, Beijing Jiaotong University, Beijing 100044, China
    College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

With urban expansion and traffic environment improvement, travel chains continue to grow, and the combination of travel purposes and modes becomes more complex. The promotion of mobility as a service (MaaS) has positive effects on facilitating the public transport traffic environment. However, public transport service optimization requires an accurate understanding of the travel environment, selection preferences, demand prediction, and systematic dispatch. Our study focused on the relationship between the trip-chain complexity environment and travel intention, combining the Theory of Planned Behavior (TPB) with travelers’ preferences to construct a bounded rationality theory. First, this study used K-means clustering to transform the characteristics of the travel trip chain into the complexity of the trip chain. Then, based on the partial least squares structural equation model (PLS-SEM) and the generalized ordered Logit model, a mixed-selection model was established. Finally, the travel intention of PLS-SEM was compared with the travel sharing rate of the generalized ordered Logit model to determine the trip-chain complexity effects for different public transport modes. The results showed that (1) the proposed model, which transformed travel-chain characteristics into travel-chain complexity using K-means clustering and adopted a bounded rationality perspective, had the best fit and was the most effective with comparison to the previous prediction approaches. (2) Compared with service quality, trip-chain complexity negatively affected the intention of using public transport in a wider range of indirect paths. Gender, vehicle ownership, and with children/without children had significant moderating effects on certain paths of the SEM. (3) The research results obtained by PLS-SEM indicated that when travelers were more willing to travel by subway, the subway travel sharing rate corresponding to the generalized ordered Logit model was only 21.25–43.49%. Similarly, the sharing rate of travel by bus was only 32–44% as travelers were more willing to travel by bus obtained from PLS-SEM. Therefore, it is necessary to combine the qualitative results of PLS-SEM with the quantitative results of generalized ordered Logit. Moreover, when service quality, preferences, and subjective norms were based on the mean value, with each increase in trip-chain complexity, the subway travel sharing rate was reduced by 3.89–8.30%, while the bus travel sharing rate was reduced by 4.63–6.03%.

Suggested Citation

  • Yuan Yuan & Chunfu Shao & Zhichao Cao & Chaoying Yin, 2023. "The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model," IJERPH, MDPI, vol. 20(5), pages 1-29, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4547-:d:1087362
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    References listed on IDEAS

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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Liao, Feixiong & Tian, Qiong & Arentze, Theo & Huang, Hai-Jun & Timmermans, Harry J.P., 2020. "Travel preferences of multimodal transport systems in emerging markets: The case of Beijing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 250-266.
    3. Daniel McFadden, 1986. "The Choice Theory Approach to Market Research," Marketing Science, INFORMS, vol. 5(4), pages 275-297.
    4. David Hensher & April Reyes, 2000. "Trip chaining as a barrier to the propensity to use public transport," Transportation, Springer, vol. 27(4), pages 341-361, December.
    5. Huang, Arthur & Levinson, David, 2017. "A model of two-destination choice in trip chains with GPS data," Journal of choice modelling, Elsevier, vol. 24(C), pages 51-62.
    6. Richard Williams, 2006. "Generalized ordered logit/partial proportional odds models for ordinal dependent variables," Stata Journal, StataCorp LP, vol. 6(1), pages 58-82, March.
    7. Md Hadiuzzaman & Nahid Parvez Farazi & Sanjana Hossain & Saurav Barua & Farzana Rahman, 2019. "Structural equation approach to investigate trip-chaining and mode choice relationships in the context of developing countries," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(4), pages 391-415, May.
    8. Cong Qi & Zhenjun Zhu & Xiucheng Guo & Ruiying Lu & Junlan Chen, 2020. "Examining Interrelationships between Tourist Travel Mode and Trip Chain Choices Using the Nested Logit Model," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    9. Eldeeb, Gamal & Mohamed, Moataz, 2020. "Quantifying preference heterogeneity in transit service desired quality using a latent class choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 119-133.
    10. Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
    11. Lai, Wen-Tai & Chen, Ching-Fu, 2011. "Behavioral intentions of public transit passengers--The roles of service quality, perceived value, satisfaction and involvement," Transport Policy, Elsevier, vol. 18(2), pages 318-325, March.
    12. Kuo, Yong-Hong & Leung, Janny M.Y. & Yan, Yimo, 2023. "Public transport for smart cities: Recent innovations and future challenges," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1001-1026.
    13. Allen, Jaime & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2019. "Understanding public transport satisfaction: Using Maslow's hierarchy of (transit) needs," Transport Policy, Elsevier, vol. 81(C), pages 75-94.
    14. Hensher, David A., 2017. "Future bus transport contracts under a mobility as a service (MaaS) regime in the digital age: Are they likely to change?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 86-96.
    15. Xinshu Zhao & John G. Lynch & Qimei Chen, 2010. "Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 197-206, August.
    16. Ye, Xin & Pendyala, Ram M. & Gottardi, Giovanni, 2007. "An exploration of the relationship between mode choice and complexity of trip chaining patterns," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 96-113, January.
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