Multi-Modal Assessment of Road Roughness Using Smartphone Applications, Acceleration, and Passenger Ratings
For transportation agencies and researchers, this work highlights the limitations of consumer-grade sensing for road roughness monitoring, showing that while smartphone apps correlate well, systematic bias prevents interchangeability.
This study evaluates a multi-modal, low-cost road roughness assessment framework using smartphone IRI estimates, GNSS-IMU data, and passenger ratings across 1700 km. Results show strong inter-app correlation but systematic bias, and confirm perceptual sensitivity to roughness and physical linkage between IRI and vertical acceleration.
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.