Gaussian Processes for Imputation of Missing Traffic Volume Data

Despite the constant innovations of modern traffic counting devices, missing data is still a major concern for most of continuous traffic monitoring programs. In this work, it is proposed an algorithm for imputation of missing data obtained from continuous traffic counting devices using Gaussian Processes techniques for Machine Learning applied to time-series data. The authors present some applications of the method for the treatment of data from the Brazilian National Traffic Counting Plan (PNCT), under the responsibility of the Brazilian National Department of Transport Infrastructure (DNIT). The tests show that the PNCT data reconstructions obtained with the algorithm proposed in this work are robust and accurate. The method is flexible enough to consistently model widely varying traffic patterns, without loss of interpretability. Another important characteristic of the proposed method is the ability of generating confidence intervals for every imputed data. In the end of the work, the authors also discuss some possible future extensions for predicting traffic volumes, and to learn joint traffic patterns for different sensors.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Authors:
  • Ramos, Fabio
  • Mirandola, Heudson
  • Picciani, Douglas
  • Ribeiro, Glaydston Mattos
  • Ivanova, Ivani
  • Quadros, Saul Germano Rabello
  • Filho, Romulo Dante Orrico
  • Perim, Leonardo R
  • Abramides, Carlos A
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 15p

Subject/Index Terms

• TRT Terms: Algorithms; Information processing; Machine learning; Time series; Traffic counts; Traffic volume
• Geographic Terms: Brazil
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01658946
• Record Type: Publication
• Report/Paper Numbers: 18-04912
• Files: TRIS, TRB, ATRI
• Created Date: Feb 5 2018 11:24AM