Integration of Smart-Phone-Based Pavement Roughness Data Collection Tool With Asset Management System

Buttlar, William G.; Islam, Md. Shahidul · 2014 · ROSA P / NEXTRANS Center (U.S.)

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Summary

This study addresses the high costs and logistical challenges associated with traditional pavement roughness data collection, which relies on expensive, specialized data collection vehicles. With shrinking maintenance budgets, transportation agencies often collect roughness data infrequently, leading to outdated asset management information. The research investigates whether smartphone-based accelerometers and GPS can serve as a cost-effective, crowd-sourced alternative for measuring the International Roughness Index (IRI), a standard metric for pavement condition. The primary objective was to develop and validate an Android-based application, "Roughness Capture," capable of estimating IRI by processing vehicle vertical acceleration data. The methodology involved field testing on three 2-mile segments of county highways in Illinois, selected to represent a range of pavement conditions from smooth to severely rough. Data collection utilized a Honda CRV equipped with an industry-standard inertial profiler to serve as the reference system. Simultaneously, a Samsung Galaxy smartphone running the Roughness Capture application was mounted on the dashboard to record vertical acceleration, timestamps, and GPS coordinates at a rate of 100 samples per second while driving at 50 mph. The collected acceleration data was processed using a MATLAB script to double-integrate the values into a pavement profile, which was then analyzed using ProVAL software to estimate IRI. Repeatability was assessed by conducting multiple runs over the same sections. The results demonstrated that the smartphone application produced IRI values closely correlated with the inertial profiler for pavements in good to fair condition, often without requiring system calibration. For these sections, most measurements fell within a 10 inch/mile offset of the reference values, indicating that the smartphone data would yield the same pavement management decisions. However, on the roughest test site, the uncalibrated smartphone system underpredicted IRI, likely due to vehicle suspension damping and sampling limitations. A simple linear calibration equation successfully corrected this discrepancy, aligning the smartphone results with the profiler. Repeatability tests showed acceptable consistency, with coefficients of variation (COV) averaging 9–11% across sites, though slightly higher than the <5% COV typical of inertial profilers. The study concludes that smartphone-based roughness measurement is a viable, economical tool for pavement asset management, particularly for networks with moderate roughness. While vehicle suspension characteristics and sampling rates affect accuracy on rough surfaces, these issues can be mitigated through calibration or improved modeling. The findings suggest that crowd-sourced data collection could significantly reduce costs and enable more frequent, real-time monitoring of pavement conditions, enhancing the efficiency of transportation infrastructure management.

Key finding

Smartphone-based IRI measurements closely matched inertial profiler data for smooth to fair pavements without calibration, while rougher pavements required a simple linear calibration to achieve comparable accuracy.

Methodology

field_study

Sample size: 3

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).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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