@misc{70d9e788221247f2b07c2eccef77c6cf,
title = "Ubiquitous Traffic Volume Estimation through Machine Learning Procedure",
abstract = "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.",
keywords = "machine learning models, probe vehicle data, traffic, volume estimation",
author = "Venu Garikapati",
year = "2019",
language = "American English",
series = "Presented at the 2019 Vehicle Technologies Office Annual Merit Review and Peer Evaluation Meeting, 10-13 June 2019, Arlington, Virginia",
type = "Other",
}