  Pavement rutting poses a significant challenge in flexible pavements,
necessitating costly asphalt resurfacing. To address this issue
comprehensively, we propose an advanced Bayesian hierarchical framework of
latent Gaussian models with spatial components. Our model provides a thorough
diagnostic analysis, pinpointing areas exhibiting unexpectedly high rutting
rates. Incorporating spatial and random components, and important explanatory
variables like annual average daily traffic (traffic intensity), asphalt type,
rut depth and lane width, our proposed models account for and estimate the
influence of these variables on rutting. This approach not only quantifies
uncertainties and discerns locations at the highest risk of requiring
maintenance, but also uncover spatial dependencies in rutting
(millimetre/year). We apply our models to a data set spanning eleven years
(2010-2020). Our findings emphasise the systematic unexplained spatial rutting
effect, where some of the rutting variability is accounted for by spatial
components, asphalt type, in conjunction with traffic intensity, is also found
to be the primary driver of rutting. Furthermore, the spatial dependencies
uncovered reveal road sections experiencing more than 1 millimeter of rutting
beyond annual expectations. This leads to a halving of the expected pavement
lifespan in these areas. Our study offers valuable insights, presenting maps
indicating expected rutting, and identifying locations with accelerated rutting
rates, resulting in a reduction in pavement life expectancy of at least 10
years.
