## 2 Related Works  
Deep learning techniques are powerful in discovering layer-by-layer complex nonlinearity in multivariate data and automatically extracting hidden and relevant complex patterns from data. They achieved remarkable success in modeling and forecasting time series data in academia and industry [11; 23; 24; 25]. Numerous deep methodologies have been employed in the literature to address traffic flow forecasting [26]. In [23], a temporal convolutional network (TCN) is proposed for traffic flow, and the Taguchi method is utilized to optimize the TCN structure. [PERSON] et al. in [27] employed a stacked auto-encoder(SAE) model to predict traffic flow. They used a greedy layerwise approach to train the model and showed the superior performance of the SAE compared to the backpropagation neural network, SVM, and random walk forecast approach. In [26], [PERSON] et al. proposed an approach using the exponential smoothing and extreme learning machine approach to forecast traffic flow. The configuration of this approach has been optimized using the Taguchi method. Results revealed that this approach exhibited satisfactory performance in traffic flow forecasting by achieving 91% and 88% accuracy rates in freeways and highways. In [28], [PERSON] et al. introduced a deep learning approach to predict traffic flow by combining a Gated Recurrent Unit (GRU) with the spatio-temporal analysis. To this end, the GRU model is applied to process the spatio-temporal feature information obtained from the time and spatial correlation analyses. Results showed the superior performance of this combined approach compared to the convolutional neural network (CNN) model and the GRU model. By using a deep belief network and kernel extreme learning machine, a short-term method for the traffic flow prediction is proposed in [29].  
During recent years, attention-driven methods have shown good efficacy for vision-based multiple-object localization and recognition, despite the training performed using labeled samples of each object [30]. Essentially, attention-based methods mimic the human vision system to recognize objects by focusing only on the object's relevant areas. However, it is worth noticing that, until recently, only a very few studies reported in the literature focused on attention-driven methods for time-series data modeling and prediction. For instance, in [31] a traffic flow prediction framework is introduced using a complex architecture including a combination of LSTM and CNN with a wide attention module. This architecture comprises dual paths: The wide attention module preprocesses input data via linear transformation followed by a self-attention layer in the first path. The second path contains a composite model formed by staking LSTM and CNN models; the two paths' outputs are also concatenated in one layer. In this approach, the feature extraction is performed via interactions between a linear model and a self-attention mechanism to predict traffic flow. The authors in [32] design a hybrid deep learning-driven model based on CNN and LSTM (called Conv-LSTM) for traffic flow forecasting. The Conv-LSTM is applied to uncover spatial-temporal features in traffic data efficiently. A bidirectional LSTM (Bi-LSTM) is employed to extract long-term temporal features. It has been shown that incorporating the attention mechanism in this approach improves forecasting performance.