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

Journal Article · 17 November 2022 · Sustainability (Basel)

DOI: https://doi.org/10.3390/su142215367 · OSTI ID:1899824

Lim, Hyeonsup ^[1]; Uddin, Majbah  ^[1]; Liu, Yuandong ^[1]; Chin, Shih-Miao  ^[1]; Hwang, Ho-Ling  ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: US Department of Transportation, Bureau of Transportation Statistics (BTS); US Department of Transportation, Federal Highway Administration (FHWA); USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1899824
• Journal Information: Sustainability (Basel), Journal Name: Sustainability (Basel) Journal Issue: 22 Vol. 14; ISSN 2071-1050
• Publisher: MDPICopyright Statement
• Country of Publication: United States
• Language: English