# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 5 Results Analysis  
The proposed model structure was trained on the experimental environment and parameter setting adopted in the experiment, as discussed in Section 4. The traffic flow and speed data were inputted into the CNN to pick up the spatial characteristics, and BILSTM was inputted to pick up the temporal characteristics. The predicted results are presented on the left-hand side of Figure 6. As observed from the figure, the actual traffic flow value fitted appropriately with the predicted value, and the overall flow trend was consistent with an evident prediction effect. Comparatively, the prediction error is charted on the right-hand side of the figure, which indicates an extremely small predicted flow error, which completely proves the accuracy and feasibility of the proposed model. Owing to the strong periodicity of traffic data and to obtain a more comprehensive understanding of the model, we further processed the data and trained the model by categorizing the data into two major components: weekly and daily cycles. Since the traffic flow in the predicted time period was often strongly correlated with its adjacent instance or the same instance, but not with the time in a distant period, we selected the traffic flow at the same instance to forecast the traffic flow in the target time period. First, we set the current time as \(t\) and the predicted time step as \(T_{p}\) = 2 h. These two time segments of the daily and weekly cycles were established as \(T_{d}\) and \(T_{w}\), respectively,  
Figure 6: Forecast results and errors. which were integer multiples of \(T_{p}\). Correspondingly, the traffic fragments were intercepted to establish the correlation matrix, where the daily cycle segment can be stated as follows:  
\[F_{d}=\left( \begin{array}{c}f_{t-(T_{d}/T_{p})\times m+1},\cdots,f_{t-(T_{d}/T_{ p})\times m+T_{p}},f_{t-(T_{d}/T_{p-1})\times m+1},\cdots,\\ f_{t-(T_{d}/T_{p-1})\times m+T_{p}},\cdots,f_{t-m+1},\cdots,f_{t-m+T_{p}}\end{array} \right) \tag{17}\]  
Similarly, the weekly cycle segment can be expressed as follows:  
\[F_{d}=\left( \begin{array}{c}f_{t-7\times(T_{d}/T_{p})\times m+1},\cdots,f_{t- 7\times(T_{d}/T_{p})\times m+T_{p}},f_{t-7\times(T_{d}/T_{p-1})\times m+1}, \cdots,\\ f_{t-7\times(T_{d}/T_{p-1})\times m+T_{p}},\cdots,f_{t-7\times m+1},\cdots,f_{t- 7\times m+T_{p}}\end{array} \right) \tag{18}\]  
Thereafter, the processed data were substituted into the model, and the data volume of 24 samples was predicted after two hours. The prediction results are illustrated in Figure 7, which further verifies the accuracy of the model and the periodicity of traffic flow. To verify the effect of time on model prediction, this study further classified the time intervals. Upon assuming that \(n\) denotes a time step, we set \(n=1\), 3, and 6 for training, i.e., the corresponding time intervals were 5, 15, and 30 min. At the same instant, we selected three models, namely BILSTM, GRU, and SVR, for comparison, and independently executed each model on 30 instances according to the time interval, packaged the results, and presumed the average value for comparison. Overall, _MAPE_, _RMSE_, and other evaluation indicators implied that the proposed model delivered superior performance to other models at time intervals of 5 min. The results are shown in Figures 8-10. Similarly, the results presented in the following Table 1 indicate that the SVR model produced the largest error in all predictions, because the SVR uses a kernel function to map numerous uncertain traffic flow data into high-dimensional space, and, consequently, fails to completely utilize the spatial and periodic characteristics for traffic prediction, thereby resulting in poor prediction performance. Upon considering the 5 min interval as a reference, the _MAE_, _MAPE_, _RMSE_, and other indicators were reduced by 47.5%, 36.2%, and 47.2%, respectively. Compared to the SVR model, the GRU model more appropriately reflects the periodicity of traffic flow by predicting the time series of traffic flow.