The increasing number of vehicles leads to different levels of traffic congestion. Although many researchers have proposed many solutions to solve the problem of traffic congestion, there is still no ideal solution to solve it. Because the chaotic complexity of the recurrent neural network (RNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU). The models are used in experiments, and their mean square error (MSE) is estimated by the evaluation criterion. According to the results of computational experiments, the GRU model is recognized as the most effective model.
| Mualliflar | Бойманов, Ислом, Сайдумаров, Илхомжан |
|---|---|
| Jurnal | Темир йўл транспорти: долзарб масалалар ва инновациялар |
| Nashr sanasi | 2025-02-17 |
| Jild | 4 |
| Betlar | 26-36 |
| Til | Rus |
Аэродром, взлетно-посадочная полоса, взлетно-посадочная полоса, эффективность, авиация, аэропорт, плоскость, проводимость., traffic flow, neural network, time series, Elman, LSTM, GRU, normalization, activation function, deep learning, test set.
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Темир йўл транспорти: долзарб масалалар ва инновациялар — barcha maqolalar