Crash and Injury Data Analysis with AI/ML

The contract seeks to leverage artificial intelligence and machine learning techniques to analyze real-world crash datasets, including NASS-CDS and other crash databases, with the goal of identifying patterns of injury severity and frequency. The work will focus on developing predictive risk functions that can inform the design and enhancement of vehicle safety standards and regulatory frameworks. This effort is intended to support evidence-based decision-making for transportation safety by uncovering hidden correlations and trends within large-scale crash data that are difficult to detect through conventional statistical methods. The contractor will be expected to apply advanced data processing, model training, and validation techniques to extract actionable insights from diverse and sometimes incomplete crash records. The project is structured as a subcontract under the Department of Transportation’s National Highway Traffic Safety Administration office, with a North American Industry Classification System code of 541690 indicating it falls under other scientific and technical consulting services. Applications must be submitted by June 23, 2026, and the performance location and point of contact details are not specified in the provided data, leaving the execution scope flexible but tightly aligned with federal safety policy objectives.