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Title	Gated Recurrent Unit (Gru) In Rnn For Traffic Forecasting Based On Time-Series Data
ID_Doc	27727
Authors	Saini K.; Sharma S.
Year	2022
Published	Proceedings - 2022 2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022
DOI	http://dx.doi.org/10.1109/CISCT55310.2022.10046484
Abstract	Traffic management has emerged as the top priority in the advancement of smart cities, and traffic prediction has been a crucial area of study in smart transportation systems that emphasize minimizing road congestion. Many new concepts have emerged as a result of vehicle networks (VNs), like mapping of traffic, traffic management, and automobile communication. Machine Learning (ML) is an effective method for discovering hidden insights in ITS without explicitly programming it by learning from data. In this work, Gated Recurrent Unit (GRU), the method in Recurrent Neural Networks is used for the time series traffic analysis and prediction. When compared with previous models, this model has shown a considerable improvement in accuracy. The findings were computed using the widely used forecasting metrics MSE, MAE, and RMSE. © 2022 IEEE.
Author Keywords	GRU; Machine learning; Mean Absolute Error; Mean Squared Error; RNN; Traffic forecasting

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