Estimating Pavement Roughness and Macrotexture Using Vehicles Equipped with Smart Tires

In the context of pavement management, conventional data collection methods for the evaluation of pavement functional condition are limited by relatively slow acquisition speeds, that prevent fast-lane motorway surveying at 120-130 km/h, and by survey frequency, which on vast networks typically occurs twice a year. Given these limitations, continuous pavement condition monitoring from moving vehicles offers an attractive solution to move towards real-time digital road assessment. In particular, such a result is achieved by making use of "intelligent" or "smart" tires, which by means of appropriate arrays of sensors can capture contact patch information, thereby providing quantitative information related to pavement roughness and macrotexture. In this study, smart tire data functional condition indicators, Dynamic Index (DI) and Pr index, were collected over several segments of a motorway network, with a total length of 405 km. Correlations were investigated between such parameters and the results of measurements coming from a traditional pavement monitoring technique, expressed in terms of international roughness index (IRI) and mean profile depth (MPD). Furthermore, the ability of smart tire indicators to identify time-dependent trends and to rank different motorway segments was assessed. Obtained results, which were generated by adopting different data processing and homogenization techniques, showed that DI displays a moderate correlation with IRI, while Pr exhibits a strong correlation with MPD. Pavement-age analysis highlighted the existence of meaningful trends for both dense-graded and open-graded asphalt-wearing courses. Motorway rankings based on average DI and Pr values were found to be in agreement with those obtained from average IRI and MPD values, thereby confirming the potential of smart tire technology as a complementary network-level monitoring tool for pavement asset management systems.