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

Raphael Piendl, Tilman Matteis and Gernot Liedtke

Transportation Research Part E: Logistics and Transportation Review, 2019, vol. 121, issue C, 149-161

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.

Keywords: Freight transport; Shipment size; Latent class analysis; Machine learning; Bayesian classification (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.tre.2018.03.005

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