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A machine learning approach for the operationalization of latent classes in a discrete shipment size choice model

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  • Piendl, Raphael
  • Matteis, Tilman
  • Liedtke, Gernot

Abstract

This paper elaborates a novel approach for implementation of latent segments concerning behaviorally sensitive shipment size choice in strategic interregional freight transport models. Discrete shipment size choice models are estimated for different homogenous segments formed by latent class analysis. A machine learning technique called Bayesian classifier is applied to link segments obtained from a sample to data of commodity flows being available on a national level. Finally, in an exemplary scenario, the impact of information and communication technologies on shipment size distributions is calculated, revealing moderate elasticities and a predominant substitution of less than truck loads by full truck loads.

Suggested Citation

  • Piendl, Raphael & Matteis, Tilman & Liedtke, Gernot, 2019. "A machine learning approach for the operationalization of latent classes in a discrete shipment size choice model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 121(C), pages 149-161.
    • Handle: RePEc:eee:transe:v:121:y:2019:i:c:p:149-161
    DOI: 10.1016/j.tre.2018.03.005
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    References listed on IDEAS

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    1. Arunotayanun, Kriangkrai & Polak, John W., 2011. "Taste heterogeneity and market segmentation in freight shippers' mode choice behaviour," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(2), pages 138-148, March.
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    6. Piendl, Raphael & Liedtke, Gernot & Matteis, Tilman, 2017. "A logit model for shipment size choice with latent classes – Empirical findings for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 102(C), pages 188-201.
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    Cited by:

    1. 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.
    2. 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.
    3. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    4. Sun, Xuting & Kuo, Yong-Hong & Xue, Weili & Li, Yanzhi, 2024. "Technology-driven logistics and supply chain management for societal impacts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    5. Luo, Suyuan & Lin, Xudong & Zheng, Zunxin, 2019. "A novel CNN-DDPG based AI-trader: Performance and roles in business operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 68-79.

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