FLAT: Smoothing the Rugged Landscape for Learnable, Sample-Efficient Traffic Calibration

Abstract: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 cannot learn, reducing sequential acquisition to near-random probing. We present FLAT, which couples what to optimize with where to sample next. An eight-dimensional behavioral fingerprint smooths the parameter-error landscape, making the objective learnable from a few dozen samples; annealed lower-confidence-bound (LCB) acquisition then spends each remaining run where it most reduces error. The surrogate, interchangeable among a Gaussian process (GP), random forest (RF), or multi-layer-perceptron (MLP) ensemble, plugs into the same LCB loop. Across six heterogeneous real-world scenes, FLAT-GP achieves the lowest scene-averaged behavioral error, winning 6/6 scenes against SPSA, GA, and CMA-ES and 5/6 against TPE under the matched budget. Some baselines need up to 4.4 times more simulations to match. Ablations show objective choice shifts final behavioral error by 81% on average, removing sequential LCB raises the six-scene mean by 20%, and surrogate choice shifts it by at most 4.2%, confirming gains trace to objective geometry and sequential allocation rather than surrogate capacity.

9 pages, 28 figures. Code and cache: this https URL

Physics and Society (physics.soc-ph); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

arXiv:2610.04337 [physics.soc-ph]

(or arXiv:2610.04337v1 [physics.soc-ph] for this version)

https://doi.org/10.48550/arXiv.2610.04337 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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