## 5 Results
### Performance Evaluation as Compared to Baseline Methods  
In this section, we compared our results with prior baseline approaches. In Table 2, we use valuation RMSE as evaluation matrices; we present our results for traffic inflow and outflow together. In Figure 5, we compare the RMSE results of our model with previous baseline methods. We evaluate our results on two real-world datasets: BJTaxi and NYCBike. Further, we run our proposed model separately for inflow and outflow to get detailed results. We also compared our results with previous baseline methods concerning inflow and outflow traffic flow. We present a detailed comparison of traffic inflow and outflow in Tables 3 and 4. On the dataset BJTaxi, our proposed variant 3 DCNN(MBSTRU) inflow RMSE is 11.49, and outflow is 11.61. The average RMSE is 11.55. On the dataset, NYCBike, inflow RMSE is 4.01 and outflow is 4.26. The average RMSE is 4.13. In our second variant, 3 DCNN(MBSTAL), we add attention-based LSTM, the RMSE is further decreased. On the dataset BJTaxi, our proposed variant 3 DCNN(MBSTAL), inflow RMSE is 11.20 and outflow is 11.42. The average RMSE is 11.31. On the dataset, NYCBike, inflow RMSE is 3.85 and outflow is 4.16. The average RMSE is 4.00. In Tables 3 and 4, we present more details of the inflow and outflow of traffic flow. Our variant 3 DCNN(MBSTRU) is outperforming as compared to the previous baseline methods. We combine all 3D CNN branches with the residual layer units results that are more effective. We extract the spatial-temporal features, merge the feature embedding branch with the external feature branch, and compare it with the previous model. We also note that our attention-based LSTM variants outperform residual unit variants. Furthermore, our variant in which we combine attention-based LSTM and the residual unit, outperforms our two variants. The variant 3 DCNN(MBSTALRU) is outperforming RMSE compared to the previous baseline methods. The performance of our variants indicates we effectively extract the spatial-temporal characteristics for traffic mobility data. If we examine Tables 2-4, we can conclude that the 3 DCNN(MBSTALRU) outperforms 3 DCNN(MBSTRU) and 3 DCNN(MBSTAL). We effectively adapt the multi-branching mechanism to increase the accuracy of the model. In contrast, from the details we represented in Tables 2-4, the traditional time series model does not achieve excellent performance. In Figures 6 and 7, we visualize the RMSE of inflow and outflow comparison with previous baseline methods on BJTaxi and NYCBike datasets. The traditional models, such as ARIMA and HA, only explore the traffic prediction based on historical values. These models do not utilize the spatial-temporal features and external factors. The regression method performs very well if we compare the results with traditional methods. The regression method explores the spatial correlations. The drawback of the regression method is it also fails to capture spatial and temporal dependence completely. Our proposed method outperforms MLP, ST-ResNet, and MGSTC. The possible reason may be MLP does not explicitly capture the spatial-temporal dependencies. The limitation of ResNet is this method only uses the 2D CNN to learn the low-level spatial features. For the temporal dependencies, take advantage of the tanh function. The MGSTC uses the multi-gated method to capture the spatial and temporal dependencies, but one of the potential reasons that the outcome is lower than our method is the time delay between input and output. Our variants perform state-of-the-art results as compared to the ConvLSTM and STDN. In this method, we adopt 2D CNN combined with LSTM together, to capture the spatial and temporal dependencies from low-level spatial feature learning.