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We analyze the accuracy of traffic simulations metamodels based on neuralnetworks and gradient boosting models (LightGBM), applied to trafficoptimization as fitness functions of genetic algorithms. Our metamodelsapproximate outcomes of traffic simulations (the total time of waiting on a redsignal) taking as an input different traffic signal settings, in order toefficiently find (sub)optimal settings. Their accuracy was proven to be verygood on randomly selected test sets, but it turned out that the accuracy maydrop in case of settings expected (according to genetic algorithms) to be closeto local optima, which makes the traffic optimization process more difficult.In this work, we investigate 16 different metamodels and 20 settings of geneticalgorithms, in order to understand what are the reasons of this phenomenon,what is its scale, how it can be mitigated and what can be potentially done todesign better real-time traffic optimization methods.

Investigating performance of neural networks and gradient boosting  models approximating microscopic traffic simulations in traffic optimization  tasks