## Literature Review
### Deep Learning-Based Approach  
Recently, in predicting traffic data, many researchers widely used deep learning-based methods. In this field, to extract the features from images, CNN has proved more effective. Thus, to consider the whole city as citywide traffic flow, treating the traffic situation as an image, CNN automatically detects the important features, which is helpful for many researchers to employ the traffic data prediction. Using CNN for predicting the traffic speed, [PERSON] [43] divided the entire city into the small grids, the city traffic speed is converted into the image and merged with CNN. To predict citywide crowd flow, rent-share, bike, and traffic flow, Reference [44] employed CNN modeling with spatial features and temporal dependent features like period, trend, and seasonal. To improve crowd flow prediction, later [45] used the residual layers with CNN and parametric-matrix-based fusion. The limitation of this study is that they only focus on the spatial correlation, to focus on modeling temporal correlation, fusion extracted features by CNN through neural networks. This approach is not sufficient to capture the temporal correlation.  
On the other hand, traffic data based on sequential learning tasks [46], the variants of recurrent neural networks (RNNs) achieved great success. These variants are LSTM and gated recurrent unit (GRU) and open new ways for researchers towards further improvement. To capture the spatial-temporal correlation to predicting the traffic flow, [PERSON] [47] proposed the cascade LSTM. The vertical dimension indicates the observation of different point index, the lateral dimension indicates the changes in the time domain. Their observation was combined with the origin-destination correlation matrix. To capture the problem of taxi demand prediction in New York City, [PERSON] [48] proposed the LSTM with mixture density. To predict the taxi demand with a probability distribution, they have marked each region of the city based on the probability distribution. Their study cannot focus on capturing the temporal correlation and also spatial correlation sufficiently.  
The prior work predicts traffic data; many researchers combined the CNN with RNN. The purpose of this combination was to make full use of spatial-temporal correlation. To capture the spatial correlation of traffic data, road network [49] is treated as a vector and then fed into the CNN. In the next stage, it is utilized two LSTMs to mine the traffic flow short-term variability. To predict the large-scale traffic flow, Reference [50] extracted the spatial features and applied deep CNN stacked with LSTM. Reference [51] believed that the entire city image hurt the prediction accuracy when applying the CNN. To solve this problem and to capture the spatial correlation, he utilized local CNN to introduce the semantic view and then combined this with LSTM to increase the prediction accuracy. Although the study captures the spatial and temporal correlation in both instances, they interacted between spatial and temporal correlation, separately.  
To overcome the above limitations, we have addressed these aspects and propose the novel multi-branching spatial-temporal 3 DCNN(MBSTALRU) for the citywide traffic flow forecasting problem. The previous study interacts with spatial and temporal data separately. Our approach captures the spatial and temporal correlation simultaneously for traffic inflow and outflow. We have also taken external factors into account to improve the accuracy of our models.