Simulation-Based Optimization of Highway Active Traffic Management Strategy Designs

Microscopic traffic simulation models are promising for modeling detailed traffic features from the individual vehicle level, but they present challenges in optimization due to their computationally time-consuming, and non-analytic nature. This study addresses the difficulty of incorporating microscopic simulations into the optimization framework for multiple types of Active Traffic Management (ATM) strategies design by adopting the Simulation-Based Optimization (SBO) paradigm. A novel discrete SBO algorithm for highway ATM strategies designs, embedded within a Cell Transmission Model (CTM)-based meta model and a problem-specific Adaptive Hyperbox Algorithm (AHA) is proposed. The effectiveness of the proposed SBO method is validated through two cases: a toy highway and a real-world highway. The method is benchmarked versus the commonly utilized general-purpose model based-SBO algorithm. The experiments indicate that the proposed problem specific SBO algorithm outperforms the general-purpose model-based algorithms to obtain promising solutions in ATM strategies design problems, especially when the simulation resources are tight. Under ATM strategies derived by the proposed algorithm, the observed total delay on the highway shows a substantial 70% decrease. The ATM strategy obtained through the microscopic model-based optimization is proven to be superior to the macroscopic model-based approach. The introduced SBO algorithm in this work advances the current SBO theoretical framework by enhancing its applicability to highway traffic issues and overcoming obstacles related to the optimization of discrete variables.