Crowd-Sensing Road Surface Quality Using Connected Vehicle Data

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.

• Record URL:
  https://doi.org/10.1177/03611981211019735
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Jinzhu Chen https://orcid.org/0000-0003-3586-598X © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Chen, Jinzhu
  • 0000-0003-3586-598X
  • Grimm, Donald K
  • 0000-0002-6444-7122
  • Bai, Fan
  • Grace, John
  • 0000-0002-7542-2174
  • Relan, Sangeeta
  • Vavrik, William
  • 0000-0002-2377-2252
• Publication Date: 2021-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 729-739
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 11
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Connected vehicles; Correlation analysis; Crowdsourcing; Data collection; Profilometers; Ride quality; Roughness
• Identifier Terms: International Roughness Index; Virginia Smart Road
• Geographic Terms: Detroit (Michigan)
• Subject Areas: Data and Information Technology; Highways; Pavements;

Filing Info

• Accession Number: 01777869
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Jul 26 2021 5:59PM