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Traffic congestion is a pressing issue worldwide, and machine learning (ML) methods are increasingly being used in Intelligent Transportation Systems (ITS) to address this problem. Deep hybrid models, in particular, have emerged as an efficient solution for traffic flow prediction. Among these models, Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) have been widely used to capture the temporal and periodic features of traffic data. The advancements in RNNs provide an opportunity to enhance the performance of existing models. Therefore, this work proposes a BiGRU-BiGRU model with two modules to extract temporal and periodic features from traffic data. Recurrent Neural Networks (RNNs) have proven to perform well using attention mechanism. However, there is a need for attention mechanism that strictly focuses on traffic dynamics and nearby data from the prediction points. Thus, a novel confined attention mechanism is proposed and incorporated into the first module to improve the model's performance by focusing only on the recent relevant information in the traffic flow sequence. Furthermore, the external features are integrated to improve the model's prediction performance. The proposed model is evaluated on the publicly available real-world dataset and compared with several baseline state-of-the-art methods. As an outcome, the model offers a reduction in the average value of RMSE, MAE, and MAPE for all the prediction horizons, that is ranged from 5.1% – 20.4%, 7.3% – 27.3%, and 6.1% – 56.6%, respectively.
Chauhan et al. (Thu,) studied this question.