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Title: Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint

Abstract

This paper examines the feasibility of using sampled commercial probe data in combination with validated continuous counter data to accurately estimate vehicle volume across the entire roadway network, for any hour during the year. Currently either real time or archived volume data for roadways at specific times are extremely sparse. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of commercial probe data to generate accurate volume measures that span the highway network providing ubiquitous coverage in space, and specific point-in-time measures for a specific date and time. The paper examines the need for the data, fundamental accuracy limitations based on a basic statistical model that take into account the sampling nature of probe data, and early results from a proof of concept exercise revealing the potential of probe type data calibrated with public continuous count data to meet end user expectations in terms of accuracy of volume estimates.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2];  [2];  [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Maryland
  3. I95 Corridor Coalition
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
University of Maryland
OSTI Identifier:
1426856
Report Number(s):
NREL/CP-5400-70938
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the ITS World Congress 2017, 29 October - 2 November 2017, Montreal, Canada
Country of Publication:
United States
Language:
English
Subject:
25 ENERGY STORAGE; volume estimation; probe data; neural networks; machine learning

Citation Formats

Hou, Yi, Young, Stanley E, Sadabadi, Kaveh, SekuBa, PrzemysBaw, and Markow, Denise. Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint. United States: N. p., 2018. Web.
Hou, Yi, Young, Stanley E, Sadabadi, Kaveh, SekuBa, PrzemysBaw, & Markow, Denise. Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint. United States.
Hou, Yi, Young, Stanley E, Sadabadi, Kaveh, SekuBa, PrzemysBaw, and Markow, Denise. Tue . "Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint". United States. https://www.osti.gov/servlets/purl/1426856.
@article{osti_1426856,
title = {Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint},
author = {Hou, Yi and Young, Stanley E and Sadabadi, Kaveh and SekuBa, PrzemysBaw and Markow, Denise},
abstractNote = {This paper examines the feasibility of using sampled commercial probe data in combination with validated continuous counter data to accurately estimate vehicle volume across the entire roadway network, for any hour during the year. Currently either real time or archived volume data for roadways at specific times are extremely sparse. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of commercial probe data to generate accurate volume measures that span the highway network providing ubiquitous coverage in space, and specific point-in-time measures for a specific date and time. The paper examines the need for the data, fundamental accuracy limitations based on a basic statistical model that take into account the sampling nature of probe data, and early results from a proof of concept exercise revealing the potential of probe type data calibrated with public continuous count data to meet end user expectations in terms of accuracy of volume estimates.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {3}
}

Conference:
Other availability
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