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A behavioral analysis of freight mode choice decisions

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  • Amir Samimi
  • Kazuya Kawamura
  • Abolfazl Mohammadian

Abstract

This paper develops a behavioral analysis of freight mode choice decisions that could provide a basis for an acceptable analytical tool for policy assessment. The paper specifically examines the way that truck and rail compete for commodity movement in the US. Two binary mode choice models are introduced in which some shipment-specific variables (e.g. distance, weight and value) and mode-specific variables (e.g. haul time and cost) are found to be determinants. The specifications of the non-selected choice are imputed in a machine learning module. Shipping cost is found to be a central factor for rail shipments, while road shipments are found to be more sensitive to haul time. Sensitivity of mode choice decisions is further analyzed under different fuel price fluctuation scenarios. A low level of mode choice sensitivity is found with respect to fuel price, such that even a 50% increase in fuel cost does not cause a significant modal shift between truck and rail.

Suggested Citation

  • Amir Samimi & Kazuya Kawamura & Abolfazl Mohammadian, 2011. "A behavioral analysis of freight mode choice decisions," Transportation Planning and Technology, Taylor & Francis Journals, vol. 34(8), pages 857-869, June.
    • Handle: RePEc:taf:transp:v:34:y:2011:i:8:p:857-869
    DOI: 10.1080/03081060.2011.600092
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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
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    9. Johansson, Magnus & Vierth, Inge & Holmgren, Kristina & Cullinane, Kevin, 2023. "How will electrification and increased use of new fuels affect the effectiveness of freight modal shift policies?," Working Papers 2023:4, Swedish National Road & Transport Research Institute (VTI).
    10. Holguín-Veras, José & Kalahasthi, Lokesh & Campbell, Shama & González-Calderón, Carlos A. & (Cara) Wang, Xiaokun, 2021. "Freight mode choice: Results from a nationwide qualitative and quantitative research effort," Transportation Research Part A: Policy and Practice, Elsevier, vol. 143(C), pages 78-120.
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    13. Thi Yen Pham & Gi-Tae Yeo, 2018. "A Comparative Analysis Selecting the Transport Routes of Electronics Components from China to Vietnam," Sustainability, MDPI, vol. 10(7), pages 1-18, July.
    14. De, Arijit & Mogale, D.G. & Zhang, Mengdi & Pratap, Saurabh & Kumar, Sri Krishna & Huang, George Q., 2020. "Multi-period multi-echelon inventory transportation problem considering stakeholders behavioural tendencies," International Journal of Production Economics, Elsevier, vol. 225(C).
    15. Carrillo Murillo, David Guillermo & Liedtke, Gernot, 2013. "A model for the formation of colloidal structures in freight transportation: The case of hinterland terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 55-70.
    16. Feo-Valero, María & Arencibia, Ana Isabel & Román, Concepción, 2016. "Analyzing discrepancies between willingness to pay and willingness to accept for freight transport attributes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 151-164.
    17. Shiqi Li & Maoxiang Lang & Xueqiao Yu & Mingyue Zhang & Minghe Jiang & Sangbing Tsai & Cheng-Kuang Wang & Fang Bian, 2019. "A Sustainable Transport Competitiveness Analysis of the China Railway Express in the Context of the Belt and Road Initiative," Sustainability, MDPI, vol. 11(10), pages 1-30, May.
    18. Verena Maria Stockhammer & Sarah Pfoser & Karin Markvica & Jürgen Zajicek & Matthias Prandtstetter, 2021. "Behavioural Biases Distorting the Demand for Environmentally Friendly Freight Transport Modes: An Overview and Potential Measures," Sustainability, MDPI, vol. 13(21), pages 1-34, October.

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