Overview
This webinar discussed upcoming research findings and publications on STP, plus lessons learned from early adopters.
The search for traffic congestion solutions continues to evolve with advances in technology and a better understanding of traveler behavior. Consequently, transportation agencies are evolving towards a proactive management approach that is capable of delaying or eliminating traffic flow breakdowns.
A key aspect of proactive management is short-term prediction of traffic conditions. Short-term prediction (STP) is a real-time prediction capability that uses both historical and current data to forecast future traffic conditions for a pre-defined prediction time horizon in the near-term (e.g., the next 10, 20, 40, 60 minutes).
The ability to conduct STP is essential for moving up the Active Transportation and Demand Management (ATDM) continuum to a state of truly proactive management. STP also enhances integrated management as agencies have enough time to share information and take coordinated, proactive actions. Register below to learn about this solution from both FHWA and public agencies who are implementing it.
Speakers:
- FHWA Short-Term Traffic Prediction Project Team
- James Colyar, FHWA
- David Hale, Senior Transportation Project Manager, Leidos
- Dr. Mohammed Hadi, Florida International University
- Kelly Maurer, Director of Public Works, Cranberry Township, PA on the Cranberry Township Short-Term Prediction System
- Hari Sripathi, Director, Office of Strategic Innovation, Virginia Department of Transportation
Recording
Presentations
Short-Term Prediction Project, David Hale and Mohammed Hadi | Northern Virginia Artificial Intelligence-Based Decision Support System, Hari Sripathi | Cranberry Township Short-Term Prediction System and Lessons Learned, Kelly Maurer |