Author(s): Lucas Ferreira Monteiro

Abstract: Rural road networks play a critical role in connecting agricultural communities, facilitating access to markets, healthcare, and education, yet their condition is rarely monitored due to high survey costs and limited technical capacity. Conventional road road roughness assessment relies on specialized profilers and trained personnel, making frequent evaluations impractical in low-resource settings. Recent advances in smartphone technology provide an opportunity to leverage embedded accelerometers, gyroscopes, and global positioning systems for large-scale, low-cost road condition monitoring. This research presents a practical validation of smartphone-based road road roughness mapping on rural roads, focusing on affordability, scalability, and field applicability. Smartphones mounted in ordinary vehicles were used to collect vibration and positional data across multiple rural road segments representing varying surface conditions. Signal processing techniques were applied to derive road roughness indicators, which were then compared with reference visual ratings and standard road roughness indices to evaluate accuracy and reliability. The results demonstrate that smartphone-derived metrics show strong correlations with conventional road roughness measures, particularly when appropriate filtering, speed normalization, and sensor calibration are applied. Variability due to vehicle type, mounting position, and driving behavior was observed but remained within acceptable limits for network-level assessment. The proposed approach significantly reduces data acquisition costs while enabling continuous, crowd-sourced monitoring of rural road conditions. By validating the method under real-world rural operating conditions, this research highlights the feasibility of integrating smartphone-based road roughness mapping into routine maintenance planning and asset management frameworks. The findings support the use of widely available consumer devices as a practical alternative to expensive survey equipment, offering transportation agencies and local governments a data-driven tool to prioritize maintenance interventions, improve road safety, and enhance connectivity in underserved rural regions. Future research should refine calibration standards, expand geographic coverage, and explore integration with geographic information systems and machine learning models for automated, real time road quality classification globally.

DOI: 10.22271/27078329.2026.v5.i1a.61

Pages: 06-09 | Views: 71 | Downloads: 35

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