A low-cost system for the assessment of the road surface quality - Russotto, Salvatore; Migliore, Marco; Orlando, Salvatore; Pirrotta, Antonina (Frontiers in Built Environment, 2025)
The condition of road pavements significantly impacts transportation systems, affecting driving comfort, road safety, vehicle durability, and fuel consumption. Consequently, effective road maintenance represents a crucial task for governmental agencies, necessitating reliable, scalable, and preferably cost-effective assessment tools. Commonly employed indices, specifically the Pavement Condition Index (PCI) and the International Roughness Index (IRI), exhibit distinct limitations. Particularly, accurate IRI calculation requires expensive instrumentation, thus limiting its scalability. Regarding PCI computation, which is based on the type, number, and severity of pavement anomalies, 3D reconstruction methods, although accurate, are costly and complex, whereas visual-based methods are sensitive to environmental conditions and computationally intensive. Conversely, vibration-based methods are economical and typically involve low computational demands. However, they face challenges in anomaly classification and necessitate precise calibration for each vehicle used. Consequently, PCI calculation also suffers from limited scalability. In this paper, an innovative and cost-effective vibration-based approach is proposed for monitoring road surface quality. The proposed approach relies on a home-made low-cost acquisition unit combined with a novel algorithm that employs the Hilbert transform to calculate a new index, called Road Surface Quality Index (RSQI). The proposed methodology allows to perform large-scale data collection via cloud connectivity and generates intuitive grayscale maps that highlight road segments with poor surface quality. Experimental validations conducted along the Palermo-Altofonte route using a plug-in hybrid vehicle, together with comparative analyses against both IRI and visual-based approaches, confirm the effectiveness of the proposed approach that can be considered reliable for supporting targeted and sustainable road maintenance strategies.