Directional Grid-Based Search for Simulation Metamodeling Using Active Learning

Within dense urban environments, real-world transportation systems are often associated with extraordinary modeling complexity. Where standard analytic methods tend to fail, simulation tools emerge as reliable approaches to study such systems. Despite their versatility, simulation models can prove to be computational burdens, exhibiting prohibitive simulation runtimes. To address this shortcoming, metamodels are used to aid in the simulation modeling process.