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

Predicting times of waiting on red signals using BERT