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

Contraflow operation is frequently used for reducing traffic congestion nea r tunnels and bridges where traffic demands from the opposite directions va ry periodically. In this work, a generic real-time optimal contraflow contr ol method has been introduced. The introduced method integrates two importa nt functional components: 1) an intelligent system with artificial neural n etwork and fuzzy pattern recognition to accurately estimate the current tra ffic demands and predict the coming traffic demands, and 2) a mixed-variabl e, multilevel, constrained optimization to identify the optimal control par ameters, Application of the developed method to a case study-dynamic contra flow traffic operation at the George Massey Tunnel in Vancouver, BC, Canada has significantly reduced traffic delay and congestion.