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A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

Author

Listed:
  • Hyeonsup Lim

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Majbah Uddin

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Yuandong Liu

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Shih-Miao Chin

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Ho-Ling Hwang

    (Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

Abstract

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

Suggested Citation

  • Hyeonsup Lim & Majbah Uddin & Yuandong Liu & Shih-Miao Chin & Ho-Ling Hwang, 2022. "A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model," Sustainability, MDPI, vol. 14(22), pages 1-25, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:15367-:d:977204
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    References listed on IDEAS

    as
    1. Krisztin, Tamás, 2018. "Semi-parametric spatial autoregressive models in freight generation modeling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 121-143.
    2. Majbah Uddin & Sabreena Anowar & Naveen Eluru, 2021. "Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 44(5), pages 543-559, July.
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