A framework for modelling an optimal dynamic toll pricing strategy for a system of managed lanes is developed. A macroscopic traffic simulation model is used to estimate the traffic states subject to initial and boundary conditions while incorporating the tolling policy. Traffic states and optimal toll actions are derived for a set of scenarios. These are used with an artificial neural network to develop a tolling policy for toll actions at each step.
The aim of this research is to develop a new tool to automatically design complex actuated traffic signal plans. The method uses an automatic programming approach, combined with a mesoscopic traffic simulation model to design and evaluate optimal intersection traffic signal plans. Thus, reducing the need of human intervention in the design process. The tool takes into consideration not only the plan parameters but also the control logic as well.