Operating transportation highway networks in real time is challenging. Planned and unplanned incidents (e.g., hazardous weather conditions, accidents, local events, and so on) on highway networks can catastrophically impact mobility and safety. Traffic operators do not know which time and which strategy to engage for mitigating non-recurrent impacts, or how to incorporate overwhelmingly increasing traffic data. Mitigating non-recurrent impacts requires accurate and ahead-of-curve real-time prediction as well as proactive operational management. Unfortunately, both are not fundamentally addressed despite decades of research.

This research project proposes to develop theories, models and algorithms of Artificial Intelligence (AI) guided by transportation network flow models, to achieve two main goals: to predict non-recurrent traffic conditions in large-scale networks at least 30 minutes ahead, and to proactively recommend operational management strategies in real-time. Prediction and operational strategies are intimately coupled. The prediction will be made by a machine that learns not only historical multi-source traffic data but also considers operational strategies that are currently being recommended, or will be recommended/engaged. Operational strategies are made and updated in real time using model-based AI with ahead-of-curve prediction. Case studies will be conducted in one small municipality network and two large-scale regional networks.

• Exploratory Advanced Research

• FY 2002-2022 / Operations / Transportation Systems Management and Operations