Demand and Supply Calibration of Dynamic Traffic Assignment Models

Abstract

The calibration of dynamic traffic assignment (DTA) models has evolved rapidly over the past decade and has been fueled by the need for applications ranging from long-term planning to real-time traffic operation. Numerous formulations and solution approaches based on either analytical or simulation-based approaches have been introduced. The aim of this paper is the documentation of the existing DTA model calibration approaches for future reference. The literature on the calibration of DTA models can be categorized on the basis of the two major components that need to be calibrated: demand and supply model estimation. Travel behavior modeling and origin–destination demand estimation problems are considered in the determination of demand models. Supply models simulate traffic dynamics, queue formation, dissipation, and spillback in either a microscopic or mesoscopic context. Early DTA model calibration efforts were based on iteration between the two demand and supply components. Recent frameworks have focused on the simultaneous calibration of both components. Therefore, different solution approaches have been addressed with various functional needs and degrees of robustness. This paper summarizes the current understanding of calibration and estimation of all input parameters for a DTA model, reviews the existing literature, and highlights the gaps that need to be addressed in future research.

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