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Truck Volume Estimation via Linear Regression Under Limited Data

Author

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  • Boilé, Maria
  • Golias, Michail

Abstract

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models.

Suggested Citation

  • Boilé, Maria & Golias, Michail, 2006. "Truck Volume Estimation via Linear Regression Under Limited Data," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 45(1).
    • Handle: RePEc:ags:ndjtrf:206780
    DOI: 10.22004/ag.econ.206780
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    References listed on IDEAS

    as
    1. Li, Lei M., 2005. "An algorithm for computing exact least-trimmed squares estimate of simple linear regression with constraints," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 717-734, April.
    2. Edlund, Ove & Ekblom, Hakan, 2005. "Computing the constrained M-estimates for regression," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 19-32, April.
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    Keywords

    Industrial Organization;

    Statistics

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