Dynamic Gradient-Based Calibration for Robust and Accurate Traffic Macrosimulation
For traffic engineers and researchers using macroscopic simulation, this work provides a more robust calibration method that maintains stability and accuracy under measurement noise.
The paper tackles the problem of unstable and inaccurate calibration in macroscopic traffic flow models. The proposed dynamic, rolling-horizon calibration framework achieves a 48% improvement in predictive accuracy over conventional static calibration on real-world I-24 MOTION data.
Robust and accurate calibration of macroscopic traffic flow models such as METANET is critical for reliable prediction and effective control. While gradient-based methods are desirable for high-dimensional parameter spaces, their application to real-world traffic scenarios is hindered by highly nonconvex optimization landscapes. Consequently, standard static calibration frequently yields parameter sets that produce unstable, unrealistic traffic dynamics, undermining confidence in the estimated parameters and compromising the simulation's utility for counterfactual scenario testing. To address this, we propose a dynamic, rolling-horizon calibration framework. By reformulating static one-time estimation as a closed-loop control problem, parameters better maintain stability and accuracy in the presence of measurement noise. Using real-world data from the I-24 MOTION testbed, this work empirically characterizes the instability of standard methods. It then shows that the proposed approach simultaneously enhances robustness to perturbations and achieves a 48% improvement in predictive accuracy over conventional static calibration.