An intelligent contraflow control method for real-time optimal traffic scheduling using artificial neural network, fuzzy pattern recognition, and optimization

Abstract

Contraflow operation is frequently used for reducing traffic congestion near tunnels and bridges where traffic demands from the opposite directions vary periodically. In this work, a generic real-time optimal contraflow control method has been introduced. The introduced method integrates two important functional components: 1) an intelligent system with artificial neural network and fuzzy pattern recognition to accurately estimate the current traffic demands and predict the coming traffic demands, and 2) a mixed-variable, multilevel, constrained optimization to identify the optimal control parameters. Application of the developed method to a case study-dynamic contraflow traffic operation at the George Massey Tunnel in Vancouver, BC, Canada-has significantly reduced traffic delay and congestion.