Investigation of the implementation of a probe-vehicle based pavement roughness estimation system.

Sauerwein, Peter M.; Smith, Brian L. · 2011 · ROSA P / Virginia Transportation Research Council

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Summary

This paper investigates the implementation of a probe-vehicle-based system for estimating pavement roughness, motivated by the need for more frequent and timely data to support maintenance decisions for aging roadway networks. The Virginia Department of Transportation (VDOT) currently relies on a contractor using dedicated sensor platforms to collect data annually for interstate and primary roads, but only every five years for secondary roads. This infrequent collection creates significant lag times and limits the accuracy of maintenance planning, particularly given VDOT’s substantial maintenance budget. The study aims to leverage the US Department of Transportation’s Intelligent Transportation Systems (ITS) initiative, specifically the connected vehicle program, to supplement existing data collection methods with a system that increases data frequency, expands lane-mile coverage, and reduces the delay between data collection and interpretation. The research evaluates the technical feasibility and characteristics of three potential system structures, all of which utilize accelerometers and wireless communications to gather pavement roughness data. The first alternative employs ITS and connected vehicle technology, utilizing Vehicle-to-Infrastructure (V2I) communications via Dedicated Short-Range Communications (DSRC). The second alternative involves installing dedicated accelerometer and communications instrument packages in agency-owned fleet vehicles. The third alternative utilizes mobile communications devices, specifically smartphones, which contain integrated accelerometers and transmit data via commercial wireless services. The analysis covers technical requirements, installation integration, and cost implications for each approach, alongside a review of current pavement condition assessment practices, including the calculation of the International Roughness Index (IRI) using the quarter-car simulation model. The study concludes that the mobile device approach is the most appropriate system structure for implementation. This method leverages smartphones to gather roughness data using their internal accelerometers and transmit the information through existing commercial wireless networks. This approach offers significant advantages over the other two alternatives, including lower costs and easier integration, as it does not require specialized infrastructure or dedicated hardware installations. The paper outlines a concept of operations for this system, detailing how it would function within VDOT’s existing pavement management framework to provide near real-time roughness estimates. While these estimates would not conform to current ASTM standards for official IRI reporting, they would provide valuable situational awareness and trend identification for maintenance decision-makers. The significance of this research lies in its potential to enhance pavement management efficiency and accuracy while reducing costs. By utilizing probe vehicles equipped with mobile devices, transportation agencies can increase the frequency of data collection and cover more roadway networks without the high expenses associated with dedicated sensor platforms. This system serves as a proof of concept for secondary applications of the connected vehicle program, demonstrating the utility of ITS data-gathering capabilities for non-safety-critical infrastructure management. The findings suggest that integrating such systems can improve the timeliness of pavement condition data, leading to more informed maintenance decisions and better allocation of resources for roadway upkeep.

Key finding

The most appropriate system for gathering pavement roughness data uses smartphone devices with integrated accelerometers and commercial wireless services.

Methodology

theoretical

Provenance

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

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• exposure measurement

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• Methodological Resource: validation psychometrics