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Traffic controllers must operate reliably across diverse traffic states. Due to the stochastic non-linear characteristics of traffic flow, commonly used feedback-based controllers require parameter tuning for each specific traffic regime, which is done offline using simulations. Generating representative traffic scenarios for large-scale simulations is often computationally expensive. To reduce the computational burden, this paper proposes a systematic exploration of the Structured Simulation Framework (SSF). This approach aims to approximate controller performance with a minimal number of simulations, by adjusting the parameter space continuously to regions where controller performances are weakly approximated. This process continues
until controller performance is well approximated across the entire input domain. Results show SSF convergence of performance estimate of the controller while reducing the number of required simulations. This helps identify traffic scenarios where the controller performs poorly, and, thus, can be used as a framework towards guided controller tuning.
Structured simulations; Function approximation; Microscopic Traffic Simulation; Traffic control
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