Estimate traffic control patterns using a hybrid neural network

Abstract

Many operating agencies are currently developing computerized freeway traffic management systems to support traffic operations as part of the intelligent transportation system (ITS) user service improvements. This study illustrates the importance of using simplified data analysis and presents a promising approach for improving demand prediction and traffic data modeling to support pro-active control. This study found that the approach of combining advanced neural networks and conventional error correction is promising for improved ITS applications.