Evaluating roadway surface rating technologies.
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Summary
This study evaluates the feasibility and accuracy of using smartphone accelerometers to measure roadway surface roughness, comparing data from a custom Android application called DataProbe against standard International Roughness Index (IRI) measurements. Conducted by the University of Michigan Transportation Research Institute for the Michigan Department of Transportation (MDOT), the research aims to determine if low-cost smartphone technology can serve as a viable alternative or supplement to traditional IRI collection methods for pavement management. The project builds upon previous work in connected vehicle initiatives, seeking to leverage accelerometer data to monitor road conditions across state networks. The experimental design involved three phases of data collection in 2012, 2013, and 2014. In the initial phases, nine Android smartphones were installed in MDOT vehicles across seven regions, collecting accelerometer data while drivers simultaneously recorded Pavement Surface and Evaluation Rating (PASER) scores. These smartphone data points were later merged with historical IRI data for the same road segments. The 2014 phase improved methodology by collecting DataProbe and IRI data simultaneously using MDOT and UMTRI vehicles on specific routes in southeastern Michigan. The DataProbe application recorded 100 three-axis accelerometer readings per second, from which a variance metric was calculated to represent vertical vehicle movement. Key variables analyzed included accelerometer variance, vehicle speed, phone model, vehicle type, road surface type, and collection date. Statistical analysis using multiple regression and ordinal logistic regression revealed that smartphone data could significantly predict IRI values and categories. For the 2012 and 2013 datasets, models accounting for phone, vehicle, speed, surface type, and date explained approximately 45% and 43% of the variance in IRI values, respectively. The 2014 simultaneous data collection showed that accelerometer variance and vehicle speed alone accounted for 37% to 39% of IRI variance. Crucially, the study found that while smartphones could not precisely predict exact IRI values, they accurately predicted IRI categories (grouped into three or five levels) with high success rates. Prediction accuracy for three-level IRI categories improved from 68% in 2012 to 86% in 2014, while five-level category predictions rose from 71% to 83% over the same period. The study also noted that higher vehicle speeds tended to result in lower predicted IRI ratings, and that individual phone differences were significant but negligible in categorical analyses. The findings suggest that smartphone accelerometers are a promising, cost-effective tool for monitoring road roughness, particularly for predicting maintenance-relevant IRI categories rather than precise numerical values. The results support the potential for crowd-sourced data collection via connected vehicle initiatives, where widespread smartphone usage could generate continuous, web-based visuals of road conditions. While smartphones cannot fully replace traditional IRI devices for exact measurement, they offer a scalable solution for frequent monitoring of local and state roadways, aiding asset management decisions.
Key finding
Smartphone accelerometer data accurately predicted three-level IRI categories 86 percent of the time and five-level IRI categories 83 percent of the time in 2014, demonstrating high reliability for categorical road condition assessment.
Methodology
field_study
Sample size: 6000
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 24 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Methodological Resource: validation psychometrics