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Crowd-Sensing Road Surface Quality Using Connected Vehicle Data
- Source :
- Transportation Research Record: Journal of the Transportation Research Board. 2675:729-739
- Publication Year :
- 2021
- Publisher :
- SAGE Publications, 2021.
-
Abstract
- This work presents an approach for collecting road surface data using connected vehicles. Road surface readings from multiple production vehicles were collected and aggregated to estimate road roughness measured by the International Roughness Index (IRI). The analysis compared multiple instances of connected vehicle data with high speed pavement profile vehicle (Class 1 profiler) data. A separate analysis compared multiple instances of connected vehicle data to an advanced walking profiler. Results demonstrate the feasibility of harvesting road surface data from the existing connected vehicles to support continuous road surface monitoring applications. Benefits include more timely acquisition of pavement data, broader coverage of the road network, and potential for aiding existing survey fleet in targeting early signs of pavement degradation. Collected roughness measurements were found to be closely aligned with reference devices that were employed as part of this study. A regional experiment in the Detroit Metropolitan area that covered 64 mi of roadways found that the connected vehicle data was highly correlated with Class 1 profiler data where 83% of traveled miles had a 0.8 or higher correlation. Moreover, 85% of the measurements had small absolute errors less than 50 in./mi and half of the measurements had absolute errors less than 20 in./mi. A test track experiment at Virginia Tech Transportation Institute Smart Road facility compared the connected vehicle data to the advanced walking profiler and showed that the correlations for repeatability and reproducibility are 0.90 and 0.91, respectively, which are very close to the standard requirement for certified profilers.
- Subjects :
- 0209 industrial biotechnology
International Roughness Index
Data collection
Computer science
business.industry
Mechanical Engineering
media_common.quotation_subject
0211 other engineering and technologies
02 engineering and technology
Surface finish
Ride quality
Crowdsourcing
Automotive engineering
020901 industrial engineering & automation
Road surface
021105 building & construction
Quality (business)
Profilometer
business
Civil and Structural Engineering
media_common
Subjects
Details
- ISSN :
- 21694052 and 03611981
- Volume :
- 2675
- Database :
- OpenAIRE
- Journal :
- Transportation Research Record: Journal of the Transportation Research Board
- Accession number :
- edsair.doi...........f844019f6f775c7b33b160afd7af5d28