  We present a method for approximating outcomes of road traffic simulations
using BERT-based models, which may find applications in, e.g., optimizing
traffic signal settings, especially with the presence of autonomous and
connected vehicles. The experiments were conducted on a dataset generated using
the Traffic Simulation Framework software runs on a realistic road network. The
BERT-based models were compared with 4 other types of machine learning models
(LightGBM, fully connected neural networks and 2 types of graph neural
networks) and gave the best results in terms of all the considered metrics.
