## 4 Results and Discussion
### Comparison with Other Models  
The developed model was compared with other popular models such as the support vector machine for regression (SVR) and the linear regression (LR) models. These models generated two statistical equations based on model parameters. Testing data were used for model validation; these equations are shown below:  
\[ \begin{array}{ll}\text{Traffic CO}=&0.0022\;\times\;\text{Number of vehicles}+\;0.0403\;\times\;\text{Number of heavy vehicles}\\ &-\;0.0187\;\times\;\text{Number of Motorbikes}\;+\;0.1957\;\times\;\text{Temperature}\\ &-\;0.0984\;\times\;\text{Wind speed}\;-\;0.0102\;\times\;\text{DSM}\;-\;4.4382 \end{array}  \tag{4}\]  
For the LR model:  
\[ \begin{array}{ll}\text{Traffic CO}=&0.05\;\times\;\text{Number of heavy vehicles}+0.21\\ \end{array}  \tag{5}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Traffic CO Predictors**} & \multicolumn{4}{c}{**Estimated Coefficient**} \\ \cline{2-5}  & **Morning** & **Afternoon** & **Evening** & **Night** \\ \hline Number of vehicles & \(-0.0016\) & \(0.0142\) & \(0.0108\) & \(0.0147\) \\ Number of heavy vehicles & \(0.0622\) & \(0.01\) & \(0.0319\) & \(-0.0216\) \\ Number of motorbikes & \(0.0135\) & \(-0.0378\) & \(-0.0376\) & \(-0.0093\) \\ Temperature \({}^{\circ}\)C & \(-0.4501\) & \(0.5512\) & \(0.4888\) & \(-0.0333\) \\ Wind speed mph & \(0.0752\) & \(-0.194\) & \(-0.4084\) & \(0.0135\) \\ DSM m & \(-0.2085\) & \(0.213\) & \(0.0812\) & \(0.1116\) \\ Intercept & \(16.8559\) & \(-22.2525\) & \(-15.8113\) & \(-2.1367\) \\ RMSE & \(2.914\) ppm & \(2.0347\) ppm & \(2.9817\) ppm & \(0.387\) ppm \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regression models for traffic CO prediction based on the CFS-MLP model. Figure 6: Prediction maps during the weekend at different times (morning, afternoon, evening and night). CO spatial prediction maps. The lowest accuracy of the validation was at night time (72.48%) and the highest accuracy was during the evening (92.75%). Table 7 shows the comparison between CFS-MLP, SVR and LR model. The comparative analysis was conducted by using the training data and the proposed model, support vector regression model, linear regression, land use regression model and dispersion model i.e., CALINE4 model based on many criteria not only Root mean square RMSE, but we also compared the proposed model with other baseline models based on mean absolute error (MAE), relative absolute error, root relative squared error and correlation coefficient. Results showed that the proposed model is superior to the compared models. The correlation coefficient based on our proposed model was 0.980, which is higher than the SVR (0.8668) and LR (0.851). The MAE of the proposed model was 0.896 ppm which is lower than SVR (1.640 ppm) and LR (1.851 ppm). On the other hand, the RMSE results indicated that the proposed model has the lowest RMSE (1.286 ppm) compared to SVR (2.752 ppm) and LR models (2.849 ppm). The RAE ratio for the proposed model was calculated to be 21.99%, which was lower than the RAE ratio of SVR (51.646%) and LR (55.048%). The root relative squared error indicated that the proposed model has the lowest value (19.40%) among the other models (49.784% and 48.292%). Table 7 shows the comparison between CFS-MLP, SVR and LR model. The comparative analysis was conducted by using the training data and the proposed models, i.e., SVR, LR, land use regression model and dispersion model, i.e., CALINE4 model based on many criteria, not only RMSE. We also compared the proposed model with other baseline models based on mean absolute error (MAE), relative absolute error, root relative squared error and correlation coefficient. Results showed that the proposed model is superior to the other models. The CO emission prediction rate can be justified as highly accurate, where the accuracy is more than 90%; 90% to 80% is a good forecast; 80% to 50% is a reasonable forecast; and more than 50% is an inaccurate forecast [48; 49; 50].