Smartphone-Based IRI Estimation for Pavement Roughness Monitoring: A Data-Driven Approach

Monitoring pavement roughness is critical for minimizing vehicle damages and ensuring road user safety. Conventional roughness measurement instruments are costly and limited in surveying frequency and spatial coverage. To overcome these limitations, vehicle-mounted smartphones have been adopted to measure pavement roughness based on the dynamic responses of traversing vehicles. Nonetheless, the accuracy and consistency of the current smartphone-based approaches are affected by practical factors, including speed, vehicle type, and mounting configuration. Existing research applied deep learning to mitigate the impact of varying practical factors, but most of them was based on simulation studies. This study introduces a method of estimating the international roughness index (IRI) using smartphone-collected real vehicle response. The approach leverages a multilayer perceptron deep learning model to account for variations in practical settings. The model achieved an RMSE of 0.60 and an
$R^{2}$
of 0.79 when compared with the ground-truth IRI. The results showcase the deployability of the proposed data-driven method in crowdsourcing-based IRI surveying.