Application of dynamic traffic assignment to advanced managed lane modeling.

Abstract

In this study, a demand estimation framework is developed for assessing the managed lane (ML) strategies by utilizing dynamic traffic assignment (DTA) modeling, instead of the traditional approaches that are based on the static traffic assignment (STA). The framework includes methods for calibrating the network (supply), demand, and assignment modeling parameters. The methods were extensively tested using real-world data to assure that the DTA modeling of managed lanes reflected real-world conditions and responded reasonably to different congestion management scenarios, such as variable pricing policies and different levels of willingness to pay. This framework is standalone and independent of the utilized DTA modeling tool.

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