A Bayesian Nonparametric Approach to AADT Estimation for Bridges

Every year, the government provides several billions of dollars in the form of federal funding for transportation services in the United States. Decision making with regards to the use of these funds largely depends on performance indicators like Annual Average Daily Traffic (AADT). In this paper, Bayesian Nonparametric models are developed through machine learning for the estimation of bridge AADTs. The effect of hyperparameter choice on the accuracy of estimations produced by Bayesian Nonparametric models is also assessed. The predictions produced using the Bayesian Nonparametric approach are then compared to predictions from a popular Frequentist approach for the selected bridges. Evaluation metrics like the Mean Absolute Percentage Error (MAPE) metrics are subsequently employed in model evaluation. Based on the results, the authors recommended the best method for AADT forecasting for the selected bridges.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 21p

Subject/Index Terms

Filing Info

  • Accession Number: 01763721
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-00402
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:09AM