Off–line and on–line calibration of Dynamic Traffic Assignment Systems

Abstract

Dynamic traffic assignment (DTA) systems integrate complex transportation demand and network supply simulation models to estimate current traffic conditions, predict future network performance and generate consistent, anticipatory route guidance. Before they are applied, DTA system parameters and inputs must be calibrated to accurately reflect travel behavior and traffic dynamics. This paper presents a systematic approach that unifies the off-line and on-line calibration of DTA systems through a common framework. Off-line calibration simultaneously estimates demand and supply model parameters. The on-line calibration jointly updates in real-time the demand and supply parameter values estimated during the off-line step to better reflect prevailing conditions. The methods are general and can utilize any available traffic surveillance information (including emerging data sources, such as Automated Vehicle Identification systems or probe vehicles). The two components complement each other so that the calibration of the DTA system parameters efficiently utilizes both historical as well as real-time information. The calibration approaches are demonstrated with DynaMIT (Dynamic network assignment for the Management of Information to Travelers), using time-varying count, speed and density data obtained from standard loop detectors.