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

Project Description

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. This research produced a tool that uses traffic detector information currently being generated in the Texas Department of Transportation freeway management systems. The tool enables personnel to make real-time, short-term predictions of when and where incidents and congestion are likely to occur on the freeway network.

Researchers examined several strategies and techniques of developing short-term forecasts (i.e., up to 15 minutes into the future) of where traffic congestion and incidents were likely to form on a freeway network using real-time traffic and weather information. They developed and incorporated four prediction models for forecasting.

This project showed that historical traffic condition information, coupled with information from other sources such as weather information, can be used to generate models that forecast short-term traffic conditions. These models are highly dependent upon having good-quality data to start. Operators can use these models to identify where incidents and congestion have the potential to occur based on the current travel conditions.

Project Publications

Dynamic Traffic Flow Modeling for Incident Detection and Short-Term Congestion Prediction: Year 1 Progress Report 0-4946-1

Development of a Prototype Dynamic Congestion and Incident Prediction System 0-4946-2

Dynamic Traffic Flow Modeling for Incident Detection and Short-Term Congestion Prediction 0-4946-S

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