Dynamic Gradient-Based Calibration for Robust and Accurate Traffic Macrosimulation

TL;DR

This paper introduces a dynamic, rolling-horizon calibration framework for traffic flow models that improves robustness and accuracy by reformulating static calibration as a control problem, validated with real-world data.

Contribution

It proposes a novel dynamic calibration method that addresses nonconvexity issues, enhancing stability and predictive accuracy in traffic simulation models.

Findings

Standard methods are often unstable with real-world data.

The proposed approach improves robustness to noise.

Achieves 48% better predictive accuracy than static calibration.

Abstract

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.