Crashes on wet pavements are responsible for 25% of all crashes and 13.5% of fatal crashes in the US (Harwood et al. 1988). This number represents a significant portion of all crashes. Current methods used by the Department of Transportations (DOTs) are based on wet over dry ratios and simplified approaches to estimate hydroplaning speeds. A fraction of all wet crashes is hydroplaning; although they are related, the difference between a "wet crash" and "hydroplaning" is a wet-crash hydrodynamic-based severity scale is less compared to hydroplaning where the driver loses control. This dissertation presents a new conceptual framework design to reduce wet- and hydroplaning-related crashes by identifying locations with a high risk of crashes using systemic, data-driven, risk-based approaches and available data. The first method is a robust systemic approach to identify areas with a high risk of wet crashes using a negative binomial regression to quantify the relationship between wet to dry ratio (WDR), traffic, and road characteristics. Results indicate that the estimates are more reliable than current methods of WDR used by DOTs. Two significant parameters are grade difference and its absolute value. The second method is a simplified approach to identify areas with a high risk of wet crashes with only crash counts by applying a spatial multiresolution analysis (SMA). Results indicate that SMA performs better than current hazardous-road segments identification (HRSI) methods based on crash counts by consistently identifying sites during several years for selected 0.1 km sections. A third method is a novel systemic approach to identify locations with a high risk of hydroplaning through a new risk-measuring parameter named performance margin, which considers road geometry, environmental condition, vehicle characteristics, and operational conditions. The performance margin can replace the traditional parameter of interest of hydroplaning speed. The hydroplaning risk depends on more factors than those identified in previous research that focuses solely on tire inflation pressure, tire footprint area, or wheel load. The braking and tire-tread parameters significantly affected the performance margin. Highway engineers now incorporate an enhanced tool for hydroplaning risk estimation that allows systemic analysis. Finally, a critical review was conducted to identify existing solutions to reduce the high potential of skidding or hydroplaning on wet pavement. The recommended strategies to help mitigate skidding and hydroplaning are presented to help in the decision process and resource allocation. Geometric design optimization provides a permanent impact on pavement runoff characteristics that reduces the water accumulation and water thickness on the lanes. Road surface modification provides a temporary impact on practical performance and non-engineering measures. / Doctor of Philosophy / Crashes on wet pavements are responsible for 25% of all crashes and 13.5% of fatal crashes in the US (Harwood et al. 1988). This number represents a significant portion of all crashes. Current procedures used by DOTs to identify locations with a high number of wet crashes and hydroplaning are too simple and might not represent actual risk. A fraction of all wet crashes is hydroplaning, although they are related to the difference between a "wet crash" and "hydroplaning" is a wet crash water-vehicle interaction is less compared to hydroplaning where the driver loses control. This dissertation presents a new procedure to evaluate the road network to identify locations with a high risk of wet crashes and hydroplaning. The risk estimation process uses data collected in the field to determine the risk at a particular location and, depending on the available data a transportation agency uses, will be the approach to apply. The first statistical method estimates the frequency of wet crashes at a location. This estimate is developed by using a statistical model, negative binomial regression. This model measures the frequency of dry crashes, wet crashes, traffic, and road characteristics to determine the total number of wet crashes at a location. Results indicate that this option is more reliable than the current methods used by DOTs. They divide the number of wet crashes by the number of dry crashes. Two elements identified to influence the results are the difference in road grade and its absolute value. The second statistical method to estimate wet crashes considers crash counts by applying a statistical process, spatial multiresolution analysis (SMA). Results indicate that SMA performs better than current processes based only on the crash counts. This option can identify the high-risk location for different years, called consistency. The more consistent the method is, the more accurate is the results. A third statistical method is a novel way to estimate hydroplaning risk. Hydroplaning risk is currently based on finding the maximum speed before hydroplaning occurs. A vehicle's performance related to the water-film thickness provides an estimation method developed by (Gallaway et al. 1971), which includes rainfall intensities, road characteristics, vehicle characteristics, and operating conditions. The hydroplaning risk depends on more aspects than tire inflation pressure, tire footprint area, or vehicle load on the wheel. The braking and tire tread affect the performance margin. Highway engineers can use this improved hydroplaning risk-estimation tool to analyze the road network. Finally, a critical review showed the available solutions to reduce the probability of having a wet crash or hydroplaning on wet pavement. The recommended strategies to mitigate wet crashes and hydroplaning provide information to allocate resources based on proven, practical strategies. Road geometry design can be optimized to remove water from the road. This geometry is a permanent modification of pavement characteristics to reduce water accumulation and water thickness on the road. Road surface treatments and non-engineering measures provide temporary measures to improve vehicle performance or driver operation.