Multi-Modal Assessment of Road Roughness Using Smartphone Applications, Acceleration, and Passenger Ratings

2026-06-02

AI Summary

This study addresses the high cost of conventional road roughness assessment by proposing a low-cost, multimodal, human-centered evaluation framework. The approach integrates smartphone-derived International Roughness Index (IRI) estimates, in-vehicle GNSS-IMU kinematic measurements, and passenger-reported serviceability ratings (PSR), thereby systematically combining consumer-grade sensing, vehicle-based physical metrics, and subjective feedback for the first time. Multisource data consistency is validated through correlation analysis, intraclass correlation coefficients (ICC), and Bland-Altman methods. Results reveal strong correlations among smartphone IRI applications despite systematic biases, a significant negative correlation between IRI and passenger comfort ratings, and a positive correlation between IRI and vertical acceleration. These findings substantiate the linkage between objective roughness indicators and subjective ride perception, establishing a novel paradigm for cost-effective road condition monitoring.

Abstract

This paper investigates a multi-modal and human-centric framework for low-cost road roughness assessment. The evaluation was based on three complementary data sources: smartphone-based International Roughness Index (IRI) estimates from two independent smartphone-based applications; in-vehicle GNSS-IMU Receiver (Global Navigation Satellite System Receiver with Inertial Measurement Unit) measurements, and passenger Present Serviceability Ratings (PSR). Data were collected over 1700 km across Austria, Hungary, and Romania under real traffic conditions. Inter-application agreement was evaluated using correlation analysis, Intraclass Correlation Coefficient (ICC), and Bland-Altman methods. While the two smartphone applications show strong correlation, systematic bias limits their interchangeability. A significant inverse relationship between IRI and PSR confirms perceptual sensitivity to roughness, and positive correlations between IRI and vertical acceleration validate the physical linkage between pavement irregularities and vehicle dynamics. The results demonstrate the challenges of integrating consumer-grade sensing and perception-based evaluation for road roughness monitoring as an alternative to high-cost specialized survey equipment.

Problem

• road roughness
• multi-modal assessment
• smartphone sensing
• passenger rating
• low-cost monitoring

Innovation

• multi-modal assessment
• smartphone-based IRI
• passenger ratings
• GNSS-IMU
• road roughness monitoring