## 2 Literature Review  
Traffic forecasts have been widely studied for many decades. However, it is still an inspiring problem because of the dynamic spatial and nonlinear temporal dependencies [9; 10]. In general, the research phase has gone through two stages of growth, i.e., the data-driven stage and the mathematical model-driven stage. The frameworks are delineated unevenly into parameterized, non-parameterized, and hybrid models of integration [14]. In the older phase, forecasting destination flow was predominantly driven by mathematical models, and a parametric model was often the central element of the prediction model. The standard frameworks are time series-based statistical analysis methods, for example the Autoregressive Integrated Moving Average (ARIMA) model and its variants [15]. The ARIMA model takes into account the intrinsic significance over terms of traffic variables and incorporates self-regression for prediction [16; 17; 18; 19; 20; 21; 22; 23]. There is no need to acknowledge certain exogenous variables for their simplicity in nature and it is only driven by endogenous variables, but the time series or its distinction should be consistent. As a linear relationship model, the ARIMA model is not sufficient for explaining the association around nonlinear variables and dynamic changes. Non-parameterized models primarily include Support Vector Regression (SVR), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and most popular Artificial Neural Networks (ANN) [24]. The models belonging to ANN, particularly deep neural networks that have been progressively established in recent decades, CNN and RNN, may define complex nonlinear relationships between variables. [PERSON] et al. [25] proposed an efficient method for the forecasting of the nonlinear and non-periodic speed of traffic flow on city roads. This method is based on a deep convolutional strategy, for extremely fluctuating road segments. Neural networks achieved an average increase in prediction performance from 0.9% to 3.3% and improved consistently up to 23.8% for individual road segments [26]. The developed deep network architecture [27] Stacked Autoencoder (SAE) for traffic local highway network flow regression suggests that deep networks could better capture the features of data spatial-temporal correlation compared to traditional shallow models, such as machine learning algorithms, backpropagation neural networks, and SVM, for prediction. It could be possible to increase accuracy [28] substantially. Since CNN is able to retrieve the correlation of spatial characteristics and RNN can obtain the correlation of temporal features, the incorporation of the two has the ability to distinguish visual characteristics. It is also commonly used for the spatial-temporal study of traffic flow. [PERSON] et al. [29] designed Spatial Recurrent Convolutionary Networks (SRCNs) to transform motor vehicle speeds in urban road networks into images, estimated vehicle speed using image sequence analysis, and evaluated its greater efficiency compared to models such as SVM and SAE [30]. The authors of [31] used a deep neural hybrid network made up of CNN and a Long Short-Term Memory (LSTM) to study the GPS taxi trajectory data and predict the city-wide road network. Taking into consideration the spatial-temporal features of the traffic flow, the adjustments have expanded the conventional forecast of traffic movement from road segments to the whole city-wide road network [32]. In addition, [PERSON] et al. [33] implemented RNN to the taxi destination forecast, predicting the destination of the taxi on the basis of the GPS location of the taxi after its departure. The results indicate RNN's excellent forecast performance [34]. Deep learning technologies have been used to forecast the future of a recurrent neural network-restricted Boltzmann machine deep correlation model formulated using congestion in large-scale transport networks.