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), pavement 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 emphasize the systematic unexpected spatial rutting effect, where more of the rutting variability is explained by including spatial components. Pavement 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.

翻译：路面车辙对柔性路面构成重大挑战，常需耗费高昂成本进行沥青罩面翻修。为全面解决这一问题，我们提出一个包含空间分量的潜高斯模型高级贝叶斯分层框架。该模型通过深入诊断分析，精准定位车辙速率异常偏高的区域。通过纳入空间分量、随机分量，以及年均日交通量（交通强度）、路面类型、车辙深度和车道宽度等重要解释变量，我们的模型能够量化并估计这些变量对车辙的影响。该方法不仅能量化不确定性、识别出最需维护的高风险路段，还可揭示车辙在空间上的依赖关系（毫米/年）。我们将该模型应用于跨越十一年（2010-2020年）的数据集。研究结果强调了系统性的意外空间车辙效应——加入空间分量后，车辙变异性的解释程度显著提升。同时发现，路面类型与交通强度的交互作用是车辙形成的主要驱动因素。此外，所揭示的空间依赖性显示，某些路段的车辙深度超出年度预期值1毫米以上，导致这些区域的预期路面寿命减半。本研究提供了预测车辙分布的地图，并识别出车辙加速路段（其路面预期寿命缩减至少10年），具有重要参考价值。