This architecture for software-assisted calibration facilitates the use of traffic analysis tools that manage a significant number of input and output parameters, such as traffic simulation programs. U.S. Federal Highway Administration guidelines for applying micro-simulation modeling software encourage traffic engineers to embrace calibration, in which they reconcile simulated and field-observed traffic performance. Calibration is crucial to producing accurate forecasts from a model, but the process requires expertise and time. Simulation users nationwide, in various comprehensive surveys, consistently cite the difficulty of calibrating complex simulation models as their biggest challenge. Researchers at the University of Florida have developed an architecture for software-assisted calibration that is practical, flexible, and easy to use. The architecture, called Sensitivity Analysis, Self-Calibration, and Optimization, or SASCO, provides users easy-to-use menus to prioritize relevant input and output parameters; eliminating the need for hours of research, and minimizing the possibility of human error.
Architecture for software-assisted calibration that facilitates the use of traffic analysis tools
This architecture optimizes calibration by minimizing the discrepancy between simulated and field-measured results. Menus containing pre-vetted input choices and custom selection of relevant outputs and run times allow users to accurately calibrate models based on their selection of the most meaningful parameters. SASCO automatically documents all decisions made during the calibration process, which facilitates standardized output reporting. Related to 17156