A machine learning approach for the operationalization of latent classes in a discrete shipment size choice model
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DOI: 10.1016/j.tre.2018.03.005
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Cited by:
- 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.
- 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.
- 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.
- 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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Keywords
Freight transport; Shipment size; Latent class analysis; Machine learning; Bayesian classification;All these keywords.
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