The majority of weather-related crashes in the U.S. occur on wet pavement, potentially due to hydroplaning. However, hydroplaning potential is often not considered in network-level pavement condition assessments. This paper provides a method for evaluating hydroplaning potential based on the actual road surface and geometric properties measured using mobile Light Detection and Ranging (lidar). The lidar measurements were placed in a grid format, and water film thickness was determined based on water depth and pavement macrotexture. A Monte Carlo simulation produced a travelling speed at which hydroplaning could occur. The developed method was applied to an in-service roadway section with historical wet weather crashes. It successfully identified the critical drainage basin and determined that the travelling speed at which hydroplaning could occur is lower than the posted speed limit. This method can be integrated into network-level pavement management systems to identify roadway sections with high risk of wet weather crashes.

在美国，大多数与天气有关的事故发生在潮湿的路面上，可能是由于打滑。然而，在网络级路面状况评估中通常不考虑打滑的可能性。本文提供了一种基于实际路面和使用移动光探测和测距（激光雷达）测量的几何特性来评估滑水可能性的方法。激光雷达测量以网格格式放置，水膜厚度根据水深和路面宏观纹理确定。蒙特卡罗模拟产生了可能发生滑水的行驶速度。所开发的方法应用于具有历史潮湿天气事故的在役道路路段。它成功地确定了关键流域，并确定可能发生滑水的行驶速度低于发布的速度限制。该方法可以集成到网络级路面管理系统中，以识别具有高潮湿天气碰撞风险的道路路段。