Calibration Methods for Simulation-Based Dynamic Traffic Assignment Systems

Abstract

Dynamic Traffic Assignment (DTA) integrates complex transportation demand and network supply simulation models to estimate prevailing traffic conditions, predict future network performance and generate consistent, anticipatory route guidance. Prior to deployment, the DTA’s parameters and inputs must be calibrated to accurately reflect travel behaviour and traffic dynamics. This paper presents a unified framework for off-line and on-line DTA calibration. Off-line calibration simultaneously estimates demand and supply model parameters. On-line calibration jointly updates—in real-time—the off-line estimates in order to more accurately capture current conditions. The developed methods are flexible and can be applied to any simulation model and may utilize any available traffic surveillance information (including Automated Vehicle Identification systems, probe vehicles and other emerging data sources). The off-line and on-line components complement each other to efficiently combine historical and 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 from conventional traffic sensors.