## 6 Discussion and Conclusions  
We proposed a citywide traffic flow prediction method to predict simultaneously, and the impact of studies forecasting, traffic flow on the accuracy of inflow and outflow of traffic on two real-world datasets. Then, we obtained the findings below:  
1. Traffic congestion is one of the serious problems among many cities, such as Beijing and New York. These cities face many different pressures and challenges, like waiting time, low car speed, long traveling, etc. To forecast traffic flows in cities, many researchers attempt to influence deep learning-based methods. 2. Based on the 3 DCNN(MBSTALRU) methods, including spatial-temporal information, the RMSE of traffic flow together is 10.99. We train our model to simulate the inflow and outflow results separately. The average RMSE of inflow and outflow on BJ Taxi is 11.1. The RMSE increases 0.11 when we simulate the inflow and outflow separately. On NYCBike dataset, we simulate the traffic flow together and RMSE is 3.85. The average result of RMSE as inflow and outflow is 3.75. The RMSE is decreased by 0.10, as shown in Figure 6. In the previous study, MGSTC outperforms as compared to previous baseline methods. Our model 3 DCNN(MBSTALRU) outperforms by 55% as compared to MGSTC and by 60% as compared to the STDN. 3. For our proposed variants 3 DCNN(MBSTRU), 3 DCNN(MBSTAL), and 3 DCNN(MBSTALRU), the RMSE of 3 DCNN(MBSTAL) decreases 0.29 as compared to 3 DCNN(MBSTRU) on the BJ Taxi dataset. The RMSE of 3 DCNN(MBSTALRU) decreases by 0.21 as compared to 3 DCNN(MBSTAL) variants. Similarly, for the NYCDataset, the RMSE of 3 DCNN(MBSTAL) decreases by 0.23 as compared to 3 DCNN(MBSTRU). The RMSE of 3 DCNN(MBSTRUAL) improves by 0.13 as compared to 3 DCNN(MBSTAL). 4. Multi-branch features also create variations on the simulation results. We utilize the closeness branch (CB). We merge closeness with the daily branch (CDB), and finally, we merge them with the weekly branch (CDWB). The prediction accuracy improved. The 3 DCNN(MBSTRU) RMSE is increased by 0.24 and 0.06 on BJ Taxi and NYCBike datasets. Similarly, variant 3 DCNN(MBSTAL)  
Figure 8: For the dataset BJ Taxi, visual comparison of different variants. The left column is the inflow, and the right column is the outflow. is increased by 0.07 and 0.10. Finally, on the variant 3 DCNN(MBSTALRU), RMSE increased by 0.08 and 0.05. Our main variant 3 DCNN(MBSTALRU) outperforms all previous methods defined in the baseline methods. 5. We also explore the impact of attention-based LSTM, residual layers unit with 3D CNN convolutional block. We combine 3D CNN with residual layers unit, the average RMSE 11.60; later, we combine 3D CNN with attention-based LSTM, RMSE improves 0.31,. In this way, we combine attention-based LSTM and residual layers with 3D CNN, accuracy improves 0.31 to 0.60 as shown in Figures 7 and 8. 6. Traffic congestion is occupied mostly morning, noon, and evening time. Let us examine some at 8:30 a.m, some at 8:50. a.m, some at 9:00 a.m and some at 9:30 a.m. Most of them spent significant time on roads in the traffic jam. In modern cities, facing the most critical problem is traffic congestion. The economist estimates the cost related to traffic congestion in 2017 in the UK, US, and Germany was 4615. We can use artificial intelligence technologies to reduce the traffic congestion and reduce the cost related to traffic congestion. For example, we can use a machine-learning algorithm to design a real-time traffic signal control system, utilizing cameras placed at the road intersections. Intelligent traffic signals optimize the road traffic such as less waiting time, shorter trips, reduced traffic congestion, less pollution, and happier drivers. **Limitation of the Study**. The research still has some limitations. In the future, these limitations should be studied. For example, we will use real-time location-based social network website data such as FourSquare to make prediction accuracy more real-time.