Current traffic management strategies are based on expected conditions caused by recurring congestion (e.g. by time of day, day of week), and can be very effective when provisions are also given for reasonable variations from such expectations. However, traffic variations due to non recurrent events (e.g. crashes) can be much larger and difficult to predict, making also challenging efforts to measure and forecast their disruptive effects. This project explores a proactive approach to manage non-recurring congestion by quantifying and modeling the effects of disruptive traffic events (crashes, major sporting events, weather, etc.) at a microscopic level using a comprehensive set of data sources. A combination of resources at the Utah Traffic Lab will enable collection of a number of data sources that will be integrated for a more comprehensive network analysis. Data includes high-resolution vehicle detection and traffic controller data, live video feeds, real-time weather data, and near-real-time crash records, which can be accessed through a unique content management system created by the UofU. Outcomes from this research will lead to detailed event-based spatio-temporal congestion and safety models, ultimately enabling informed and proactive traffic management and safety countermeasures. Network operators and traffic and public safety agencies may adopt such policies to improve travel time reliability, environmental outcomes, and user safety. This project will use the Salt Lake valley as a testbed and will open new opportunities for research using integration of large datasets of disaggregated data.