Hydroplaning is a major concern on high speed roadways during heavy rainfall events. Hydroplaning tools are widely used by designers to reduce their roadway’s hydroplaning potential, therefore reducing the possibilities of severe crashes. This dissertation presents two methodologies for improving the prediction of hydroplaning potential. The first phase focused on improving an existing widely used software called PAVDRN. Using multiple datasets from the Florida Department of Transportation, the author filtered the data using specific criteria to leave only truly dynamic hydroplaning crashes. The author then evaluated PAVDRN’s prediction capabilities and assessed its reliability in predicting a hydroplaning crash. Using past accident statistics, the author accounted for extraneous factors that are difficult to capture, such as driver behavior, and obtained probability factors for a more realistic estimate of hydroplaning risk on roadways. The second phase focused on improving the modeling technique used in hydroplaning prediction tools. Currently when assessing a roadway’s hydroplaning potential, the roadside drainage is not considered in the analysis. The author modeled a combined pavement-drainage system using a 1D/2D method to better capture the effects of roadside drainage, especially in the events of flooding. The methodology used in modeling successfully captures the backwater effects that are caused under critical flooding conditions. Lastly the author created a new tool (MY-PAVDTCH) to provide design engineers with updated waterfilm thickness values under roadside drainage flooded conditions.