Dynamic Traffic Flow Modeling for Incident Detection and Short-Term Congestion Prediction Project summary

摘要

Historically, freeway traffic management software has been designed to allow operators to react to incidents and congestion after they have already occurred. While reacting to unexpected events will always remain a critical part of freeway operations, freeway operators need to proactively manage traffic on the freeway to minimize the impact of events or even possibly prevent them from occurring in the first place. The purpose of this research project was to produce a tool that the Texas Department of Transportation (TxDOT) can implement in freeway management centers that will allow use of traffic detector information currently being generated in TxDOT freeway management systems. An effective tool would enable personnel to make real-time, short-term predictions of when and where incidents and congestion are likely to occur on the freeway network. The idea was to combine roadway network modeling, traffic flow simulation, statistical regression and prediction methodologies, and archived real-time traffic sensor information to forecast when and where: traffic conditions were likely to produce an incident, and platoons of traffic would merge together to create congestion on the freeway.