Infrastructure Pavement Assessment & Management Applications Enabled by the Connected Vehicles Environment - Proof-of-Concept

Katicha, Samer; Zeng, Huanghui; Park, Hyungjun; Smith, Brian; Flintsch, Gerardo · 2015 · ROSA P / Connected Vehicle/Infrastructure University Transportation Center

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Summary

This paper presents a proof-of-concept study investigating the use of connected vehicles to assess pavement conditions, addressing the high costs and scalability limitations of traditional data collection methods. Current pavement management relies on specialized inertial profilers, which are expensive and difficult to deploy frequently across large networks. The research hypothesizes that the vertical acceleration data collected from the sensors in everyday vehicles (probe vehicles) can serve as a cost-effective, continuous source of pavement roughness data. The primary objectives were to validate the feasibility of using probe vehicle acceleration to estimate the International Roughness Index (IRI) and to determine procedures for processing this data to support network-level pavement management decisions. The study employed field tests on roadways in Virginia, comparing data from instrumented probe vehicles and smartphones against standard inertial profiler measurements. Researchers developed a numerical procedure to calculate IRI from vertical acceleration using a quarter-car model, accounting for discrepancies between full-car sensor responses and the standard quarter-car simulation. A sensitivity analysis evaluated the impact of data sampling rates and vehicle parameters (mass, stiffness, damping). For network-level applications, the team collected naturalistic driving data using smartphone applications and developed a normalized acceleration metric (NRMS) to account for varying vehicle speeds. They then applied a logistic regression model to classify pavement sections as deficient (IRI ≥ 140 in/mile) or non-deficient based on the NRMS values. The results demonstrated that roughness measures derived from probe vehicles were comparable to those from inertial profilers when appropriate parameters were applied. The sensitivity analysis identified data sampling rates and quarter-car parameters, specifically suspension damping and tire stiffness, as the most critical factors affecting calculation accuracy. In the network-level simulations, the NRMS metric showed a consistent relationship with IRI across different highway types, unlike unnormalized acceleration data. The logistic regression model achieved an 80% success rate in identifying deficient pavement sections, with an overall misclassification rate of 5.5% (nine out of 162 sections). Misclassified sections were primarily located near the deficiency threshold, indicating the model’s reliability for prescreening purposes. The study concludes that probe vehicle vertical acceleration measurements, even those collected via mobile smartphones, have significant potential for network-level prescreening of deficient pavement sections. This approach allows transportation agencies to identify areas requiring maintenance without deploying expensive profiler vans across the entire network. By using probe vehicles to flag likely deficient sections, agencies can target inertial profiler deployments more efficiently, reducing costs while maintaining effective pavement management. The authors recommend developing a prototype system using state-owned vehicles to generate comprehensive datasets for further validation and implementation assessment.

Key finding

Probe vehicle vertical acceleration measurements achieved an 80% identification rate for deficient pavement sections with a 5.5% overall misclassification rate.

Methodology

field_study

Sample size: 162

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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• exposure measurement

Information type

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• Methodological Resource: validation psychometrics