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How will China–Singapore International Land–Sea Trade Corridor affect route choice behaviour? A discrete choice model

Author

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  • Zhu, Siying
  • Cai, Yutong
  • Wang, Mengtong
  • Wang, Hua
  • Meng, Qiang

Abstract

Under the government-to-government cooperation framework, the Memorandum of Understanding (MoU) on China–Singapore International Land–Sea Trade Corridor (C–S-ILSTC) between Singapore and Chongqing has been signed in 2018, with the objective to improve the connectivity between Southeast Asia and Western China region via the Beibu Gulf port. C–S-ILSTC provides a new and efficient intermodal freight transportation route choice, which is expected to reconstruct the regional intermodal container transportation network. In this study, we formulate a discrete route choice model based on stated preference survey to analyse the route choice behaviour between Southeast Asia and western China region under the intermodal container transport network with C–S-ILSTC. Based on the modelling results, we further discuss the impact of significant route choice influential factors, such as transit time, shipping fee, cargo type, historical experience of using the route, company size, annual throughput, and business area. In addition, descriptive statistics and word cloud analysis are provided to investigate respondents’ underlying attitudes on the most important route choice influential factors. Scenario analysis is further conducted to demonstrate the impact of subsidy on respondents’ willingness to switch to the newly introduced C–S-ILSTC for freight transportation. The results provide policy implications on improving the utilisation rate of C–S-ILSTC.

Suggested Citation

  • Zhu, Siying & Cai, Yutong & Wang, Mengtong & Wang, Hua & Meng, Qiang, 2023. "How will China–Singapore International Land–Sea Trade Corridor affect route choice behaviour? A discrete choice model," Transport Policy, Elsevier, vol. 144(C), pages 11-22.
  • Handle: RePEc:eee:trapol:v:144:y:2023:i:c:p:11-22
    DOI: 10.1016/j.tranpol.2023.09.014
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    References listed on IDEAS

    as
    1. Neeraj Arora & Greg M. Allenby & James L. Ginter, 1998. "A Hierarchical Bayes Model of Primary and Secondary Demand," Marketing Science, INFORMS, vol. 17(1), pages 29-44.
    2. Vedel, Suzanne Elizabeth & Jacobsen, Jette Bredahl & Skov-Petersen, Hans, 2017. "Bicyclists’ preferences for route characteristics and crowding in Copenhagen – A choice experiment study of commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 53-64.
    3. Hensher, David A. & Balbontin, Camila & Beck, Matthew J. & Wei, Edward, 2022. "The impact of working from home on modal commuting choice response during COVID-19: Implications for two metropolitan areas in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 179-201.
    4. Lee, Sang-Jeong & Sun, Qinghe & Meng, Qiang, 2023. "Vessel weather routing subject to sulfur emission regulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    6. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    7. Jiang, Xiaodan & Fan, Houming & Luo, Meifeng & Xu, Zhenlin, 2020. "Strategic port competition in multimodal network development considering shippers’ choice," Transport Policy, Elsevier, vol. 90(C), pages 68-89.
    8. Chen, Tiantian & Fu, Xiaowen & Hensher, David A. & Li, Zhi-Chun & Sze, N.N., 2022. "The effect of online meeting and health screening on business travel: A stated preference case study in Hong Kong," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    9. Marra, Alessio D. & Sun, Linghang & Corman, Francesco, 2022. "The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area," Transport Policy, Elsevier, vol. 116(C), pages 258-268.
    10. Yan, Xiang & Zhao, Xilei & Han, Yuan & Hentenryck, Pascal Van & Dillahunt, Tawanna, 2021. "Mobility-on-demand versus fixed-route transit systems: An evaluation of traveler preferences in low-income communities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 481-495.
    11. Shaer, Amin & Haghshenas, Hossein, 2021. "Evaluating the effects of the COVID-19 outbreak on the older adults’ travel mode choices," Transport Policy, Elsevier, vol. 112(C), pages 162-172.
    12. Sun, Qinghe & Chen, Li & Meng, Qiang, 2022. "Evaluating port efficiency dynamics: A risk-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 333-347.
    13. Balbontin, Camila & Hensher, David A. & Beck, Matthew J., 2022. "Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    14. Wang, Hua & Zhang, Yiru & Meng, Qiang, 2018. "How will the opening of the Northern Sea Route influence the Suez Canal Route? An empirical analysis with discrete choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 75-89.
    15. Hasnine, Md Sami & Habib, Khandker Nurul, 2018. "What about the dynamics in daily travel mode choices? A dynamic discrete choice approach for tour-based mode choice modelling," Transport Policy, Elsevier, vol. 71(C), pages 70-80.
    16. Hensher, David A. & Beck, Matthew J. & Wei, Edward, 2021. "Working from home and its implications for strategic transport modelling based on the early days of the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 64-78.
    17. Chakraborty, Rahul & Chakravarty, Sujoy, 2023. "Factors affecting acceptance of electric two-wheelers in India: A discrete choice survey," Transport Policy, Elsevier, vol. 132(C), pages 27-41.
    18. Chen, Rui & Jia, Shuai & Meng, Qiang, 2023. "Dynamic container drayage booking and routing decision support approach for E-commerce platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    19. Yang, Dong & Jiang, Liping & Ng, Adolf K.Y., 2018. "One Belt one Road, but several routes: A case study of new emerging trade corridors connecting the Far East to Europe," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 190-204.
    20. Qinghe Sun & Li Chen & Mabel C. Chou & Qiang Meng, 2023. "Mitigating the financial risk behind emission cap compliance: A case in maritime transportation," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 283-300, January.
    21. Hong Liu & Kong Yam Tan & Guanie Lim, 2021. "Introduction — Southeast Asia And The Belt And Road Initiative: The Political Economy Of Regionalism, Trade, And Infrastructure," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 66(01), pages 1-20, March.
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