Calibration of Dynamic Traffic Assignment models
Calibration of Dynamic Traffic Assignment (DTA) models is essential in order to accurately simulate various traffic phenomena in a way that the model’s outputs reflect the real-world traffic conditions.
DTA models involve various inputs and parameters needed to be calibrated. A DTA model is divided into two components: the demand models, as well as the supply models. The demand models estimate and predict the Origin-Destination (OD) trip patterns and simulate the behavior of individual drivers. Supply models capture traffic phenomena through representations of the capacities of network elements, the speed-density relationships, etc.
The scope of this research is the development of an off-line DTA model calibration methodology for simultaneous estimation of all the demand and supply inputs and parameters, in order to minimize the error between the traffic measurements and the model’s outputs. Various solution approaches have been proposed in literature addressing the calibration of a DTA model, but this research aims at an even more accurate and efficient solution. The methodology will be generic and applicable to any DTA system, using any general traffic data. Due to the complexity and non-linearity of the calibration problem, a major issue is the selection of an appropriate optimization algorithm.
The DTA models chosen for demonstrating the methodology that will be developed are Dynameq (Dynamic Equilibrium) and Mezzo.
Advisor: Professor Haris N. Koutsopoulos, Assistant Professor Erik Jenelius, Dr. Wilco Burghout.
University: Royal Institute of Technology KTH).