  We analyze the accuracy of traffic simulations metamodels based on neural
networks and gradient boosting models (LightGBM), applied to traffic
optimization as fitness functions of genetic algorithms. Our metamodels
approximate outcomes of traffic simulations (the total time of waiting on a red
signal) taking as an input different traffic signal settings, in order to
efficiently find (sub)optimal settings. Their accuracy was proven to be very
good on randomly selected test sets, but it turned out that the accuracy may
drop in case of settings expected (according to genetic algorithms) to be close
to local optima, which makes the traffic optimization process more difficult.
In this work, we investigate 16 different metamodels and 20 settings of genetic
algorithms, 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 to
design better real-time traffic optimization methods.
