On the Input Sensitivity of METANET Models and the Robustness of Dynamic Calibration
The paper investigates how a calibrated METANET model amplifies small external disturbances at its boundaries, leading to state divergence and poor counterfactual analysis. It formalizes this sensitivity using string stability analysis, proving that dynamic calibration provides a tighter error bound than static calibration. The advantage of dynamic methods scales with the variance of real-world traffic data.