<p>Transportation safety on highways stands as a critical issue, where numerous accidents are linked to poor pavement conditions as they impact the vehicle and driver’s behaviors on the highways leading to crashes. However, the acquisition of critical pavement condition data for a comprehensive safety assessment is often challenging due to the expenses and quality constraints associated with the data. These challenges can be overcome by acquiring in-vehicular information from the Original Equipment Manufacturer (OEM) as Onboard Diagnostics (OBD). This study employed OBD data and Inertial Measurement Unit (IMU) data to evaluate pavement conditions for highway safety analysis. A comprehensive relationship between the OBD parameters, IMU parameters, pavement surface skid resistance, and crash incidents was developed using OEM data collected for this study, the skid data from the Oklahoma Department of Transportation (ODOT), and crash data from the Oklahoma highway collision database. The predictor variables (OBD, IMU, and skid data) and the response variables (crash frequency and severity) were prepared for roadway sections at 0.1-mile and 0.25-mile intervals. Crash frequency analysis and crash severity analysis were performed using count-based statistical models and machine learning approaches, respectively. It was found from this study that OEM-based data could become an efficient and cost-effective technique for pavement management through surrogate safety measures of highway safety analysis.</p>