Using Surface Geometry to Identify Potential Hydroplaning Locations

Gurganus, Charles; Devadas, Arvind; Messhenas, Saber · 2023 · ROSA P / Texas A&M Transportation Institute

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Summary

This study addresses the challenge of identifying roadway locations vulnerable to hydroplaning by analyzing surface geometry, a variable within the control of transportation agencies. Hydroplaning results from a combination of driver behavior, vehicle characteristics, and roadway features; however, agencies can mitigate risk by optimizing surface geometry and texture. The research aimed to develop a method for mapping roadway drainage basins and estimating hydroplaning potential using mobile Light Detecting and Ranging (LiDAR) data. This approach supports proactive maintenance by allowing agencies to identify and address geometric deficiencies before they contribute to high rates of wet-weather accidents. The methodology involved collecting mobile LiDAR data at highway speeds to capture detailed pavement geometry. Initially, the process was fragmented, requiring data segmentation due to file size constraints and manual processing across multiple software platforms, including MATLAB and Microsoft Excel. Researchers applied the D8 algorithm to map drainage basins and calculate water film thickness (WFT) based on simulated storm events. To streamline this workflow, the team developed a new software tool called LiPRO. LiPRO integrates data collection, processing, and analysis into a single program, utilizing a newly developed D3 algorithm to handle edge conditions and prevent artificial water sinks. The tool automatically calculates basin characteristics and hydroplaning speeds, outputting results as Google Earth files for visualization. The system was applied to Interstate Highway 20 (IH 20) in the Texas Department of Transportation’s Atlanta District, a corridor with a high incidence of wet-surface crashes. The analysis identified ten geometrically vulnerable locations where drainage basins exceeded 4,500 square feet or where the predicted hydroplaning speed was significantly lower than the posted speed limit. The study found that the most critical vulnerabilities occurred where horizontal and vertical curves coincided, causing water to struggle to shed from the pavement due to conflicting slope directions. Additionally, the unique cross-slope geometry of IH 20, which slopes across both travel lanes rather than crowning in the center, contributed to water accumulation. The significance of this work lies in its ability to provide transportation agencies with a proactive tool for hydroplaning mitigation. By identifying specific geometric vulnerabilities, the Atlanta District was able to target locations for Permeable Friction Course (PFC) treatments, which allow water to drain through the pavement surface. The development of LiPRO significantly improved the efficiency and scalability of the analysis, removing manual bottlenecks and enabling the processing of longer roadway segments. The study concludes that coupling mobile LiDAR geometry data with macrotexture measurements offers a robust method for enhancing roadway safety and reducing wet-weather accidents.

Key finding

Mobile LiDAR data can effectively identify specific roadway drainage basins with poor geometric configurations, such as combined curves and grades, that create high water accumulation and hydroplaning risk.

Methodology

field_study

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• road surface friction