Preprint Open access
FLAT: Smoothing the Rugged Landscape for Learnable, Sample-Efficient Traffic Calibration
Calibrating microscopic traffic models for digital twins is an expensive black-box optimization problem: tuning car-following and lane-changing parameters requires a full simulation run, affording only a tight budget per recalibration window. Matching raw trajectories yields a rugged objective that sparse surrogates ca …