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.
Original language | American English |
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Number of pages | 12 |
State | Published - 2018 |
Event | ITS World Congress 2017 - Montreal, Canada Duration: 29 Oct 2017 → 2 Nov 2017 |
Conference
Conference | ITS World Congress 2017 |
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City | Montreal, Canada |
Period | 29/10/17 → 2/11/17 |
NREL Publication Number
- NREL/CP-5400-70938
Keywords
- machine learning
- neural networks
- probe data
- volume estimation