Improving Work Zone Management and Safety Through AI-Powered Connected Vehicle Data Analysis

This project designed, deployed, and validated a statewide platform for real-time incident detection, daily operations analysis, and data-driven support for smart work zone planning known as ReactorIQ. Unlike traditional systems that depend on fixed roadside sensors, manual contractor logs, or 511 updates, ReactorIQ is built around high-resolution connected vehicle (CV) telemetry as its primary data source. By processing these data at scale, ReactorIQ offers a granular and behaviorally grounded view of how drivers respond to lane closures, queues, and work zone configurations in both urban and rural environments. Insights are made available through an integrated web-based dashboard where users can access CV performance metrics, lane closure detection outputs, queue warning indicators, camera-based analytics, and machine learning results within a single decision-support environment. This report details the development and key features of ReactorIQ, the challenges overcome and major accomplishments throughout the project, and directions for future work.