Abstract
Crowdsourced pavement roughness data using connected vehicles now provides cost-effective, high-resolution condition monitoring in near real-time. This study evaluates how connected vehicle data can support effective investment prioritization across Indiana’s road network and improve upon traditional data sources using surveys that are often done annually or biennially. A key challenge is handling terabyte-scale geospatial roughness datasets and integrating them meaningfully into asset management workflows. A scalable data processing methodology is outlined to address this issue and is used for the subsequent analysis.
This report’s analysis uses more than 3 billion daily CV-estimated roughness measurements (IRICVe) from 2022–2025, in addition to multiple visual condition sources: 2024 PCI data from Indianapolis and Noblesville and 2025 computer-vision PASER data from Noblesville. Statewide local paved roads show a spatial data coverage increase from 46.5% to 53.2% between 2023 and 2024. Case studies from I-65, Noblesville, and Indianapolis demonstrate the potential of these developing data sources even at the segment and route levels. Additionally, multiple metric comparisons confirm a weak roughness-surface condition correlation (with R2 of 0.15 to 0.34) and were used for network-wide screening for outlier detection and quality control. These results support implementation recommendations for using IRI and condition data together for a more comprehensive estimation of pavement quality. Although the evaluated data sources show substantial potential, they are not yet ready to replace traditional data for all use cases.
Keywords
asset management, IRI, connected vehicles, pavement condition monitoring
Report Number
FHWA/IN/JTRP-2026/04
SPR Number
4907
Sponsoring Organization
Indiana Department of Transportation
Performing Organization
Joint Transportation Research Program
Publisher Place
West Lafayette, Indiana
Date of Version
2026
DOI
10.5703/1288284318612
Recommended Citation
Thompson, A., Desai, J. C., Sakhare, R. S., & Bullock, D. M. (2026). Research support to INDOT on systemwide asset condition assessment using connected vehicle data (Joint Transportation Research Program Publication No. FHWA/IN/JTRP-2026/04). West Lafayette, IN: Purdue University. https://doi.org/10.5703/1288284318612
SPR-4907 Technical Summary