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Joint model of freight mode choice and shipment size: A copula-based random regret minimization framework

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  • Keya, Nowreen
  • Anowar, Sabreena
  • Eluru, Naveen
Abstract
In our study, we examine the joint choice of freight transportation mode and shipment size. While shipment size could be considered as an explanatory variable in modeling mode choice (or vice-versa), it is more likely that the decision of mode and shipment choice is a simultaneous process. A joint model system is developed in the form of an unordered choice model for mode and an ordered choice model for shipment size. We adopt a closed form copula-based model structure for capturing the impact of common unobserved factors affecting these two choices. Further, we explore alternatives to the traditional random utility structure in modeling mode choice. Specifically, we explore both the random utility (RU) based multinomial logit and the random regret (RR) minimization based multinomial logit (MNL) within a copula-based model. The shipment size is analyzed using ordered logit (OL) model within the copula structure. The RU and RR MNL structures are explored for several copula-based structures including Gaussian, Farlie-Gumbel-Morgenstern (FGM), Clayton, Gumbel, Frank and Joe. The proposed approach considers copula models with multiple copula-based dependencies within a single model. The copula-based model dependency is also allowed to vary across the data by parameterizing the dependency as a function of observed attributes. The models are estimated based on the data from 2012 U.S. Commodity Flow Survey data. The copula RRM based MNL-OL copula with Frank and Joe copula dependencies offered the best data fit indicating the strong interconnectedness between shipment mode and shipment size choice decisions. A validation exercise provides further evidence of the joint model superiority for overall sample level and freight characteristics variables specific sub-samples.

Suggested Citation

  • Keya, Nowreen & Anowar, Sabreena & Eluru, Naveen, 2019. "Joint model of freight mode choice and shipment size: A copula-based random regret minimization framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 97-115.
    • Handle: RePEc:eee:transe:v:125:y:2019:i:c:p:97-115
    DOI: 10.1016/j.tre.2019.03.007
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    2. Lu, Changxiang & Ye, Yong & Fang, Yongjun & Fang, Jiaqi, 2023. "An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    3. Günay, Gürkan, 2023. "Shipment size and vehicle choice modeling for road freight transport: A geographical perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    4. Zofia Gródek-Szostak & Gabriela Malik & Danuta Kajrunajtys & Anna Szeląg-Sikora & Jakub Sikora & Maciej Kuboń & Marcin Niemiec & Joanna Kapusta-Duch, 2019. "Modeling the Dependency between Extreme Prices of Selected Agricultural Products on the Derivatives Market Using the Linkage Function," Sustainability, MDPI, vol. 11(15), pages 1-14, August.
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    6. 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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    8. Raphael Piendl & Martin Koning & François Combes & Gernot Liedtke, 2022. "Building latent segments of goods to improve shipment size modeling: Confirmatory evidence from France," Post-Print hal-04117547, HAL.
    9. Sahu, Prasanta K. & Qureshi, Danish & Pani, Agnivesh, 2022. "Examining commercial vehicle fleet ownership decisions and the mediating role of freight generation: A structural equation modeling assessment," Transport Policy, Elsevier, vol. 126(C), pages 26-33.
    10. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.
    11. Kalahasthi, Lokesh & Holguín-Veras, José & Yushimito, Wilfredo F., 2022. "A freight origin-destination synthesis model with mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    12. Keya, Nowreen & Anowar, Sabreena & Bhowmik, Tanmoy & Eluru, Naveen, 2021. "A joint framework for modeling freight mode and destination choice: Application to the US commodity flow survey data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    13. 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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