Ubiquitous Traffic Volume Estimation through Machine Learning Procedure

Research output: NRELPresentation


High-quality traffic volume data is critical for transportation planning, operations, and travel-energy calculations. However, vehicle count data from roadside sensors is very sparse owing to high capital cost of installing, and on-going maintenance of electronic sensor equipment. This research effort aims to bring to market a first of its kind traffic volume data product that provides accurate estimates of traffic volumes across the entire road network for all times (24x7) and all locations (100% coverage). This product greatly improves the coverage and quality of traffic volume information by combining commercial probe traffic data with traditional traffic measurement using state-of-the-art machine learning. Commercial probe traffic data are derived from vehicles that self-report their position and speed as well as crowd sourced data from smartphone applications.
Original languageAmerican English
Number of pages27
StatePublished - 2019

Publication series

NamePresented at the 2019 Vehicle Technologies Office Annual Merit Review and Peer Evaluation Meeting, 10-13 June 2019, Arlington, Virginia

NREL Publication Number

  • NREL/PR-5400-73595


  • machine learning models
  • probe vehicle data
  • traffic
  • volume estimation


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