## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Traffic Flow**} \\ \hline \multirow{2}{*}{**Time Index**} & \multirow{2}{*}{**Actual**} & \multicolumn{6}{c}{**Predicted**} \\ \cline{3-6}  & & **Run1** & **Run2** & **Run3** & **Run4** & **Run5** \\ \hline
0 & 190 & 226 & 225 & 247 & 235 & 268 \\ \hline
10 & 137 & 143 & 142 & 119 & 80 & 92 \\ \hline
20 & 883 & 894 & 874 & 878 & 918 & 918 \\ \hline
30  
Figure 4: Traffic flow analysis of AST2 FP –OHDBN approach (**a**) Run1, (**b**) Run2, (**c**) Run3, (**d**) Run4, and (**e**) Run5. Figure 6 demonstrates a comparative inspection of the AST2 FP-OHDBN approach with existing models in terms of MSE. The figure denoted the AST2 FP-OHDBN method has gained effectual outcome over other models with minimal values of MSE under all lags. For example, with lag = 1, the AST2 FP-OHDBN approach has rendered a reduced MSE of 15.4770. Conversely, the LS-SVM system, GA-LSSVM approach, PSO-LSSVM method, FFO-LSSVM technique, and hybrid LSSVM models have offered an increased MSE of 38.3663, 32.3363, 24.7114, 22.3607, and 21.8427, correspondingly. Meanwhile, with lag = 5,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No. of** & **LS-SVM** & **GA-** & **PSO-** & **FFO-** & **Hybrid-** & **AST2 FP-OHDBN** \\ \hline \multicolumn{8}{c}{**Root-Mean-Square Error**} \\ \hline Lag = 1 & 53.8171 & 42.6758 & 39.5084 & 38.0203 & 32.7534 & 26.9257 \\ \hline Lag = 2 & 57.2355 & 46.9715 & 42.8572 & 41.0955 & 29.5013 & 23.6656 \\ \hline Lag = 3 & 61.4591 & 51.706 & 47.4003 & 45.2506 & 22.8315 & 16.5139 \\ \hline Lag = 4 & 95.8237 & 55.3336 & 51.0474 & 49.1178 & 27.9114 & 22.7806 \\ \hline \multicolumn{8}{c}{**Mean Absolute Error**} \\ \hline Lag = 1 & 38.3663 & 32.3363 & 24.7114 & 22.3607 & 21.8427 & 15.4770 \\ \hline Lag = 2 & 45.7351 & 39.6895 & 32.0517 & 25.2410 & 19.2498 & 12.3658 \\ \hline Lag = 3 & 51.3024 & 45.2973 & 39.4341 & 31.6977 & 23.5686 & 17.8232 \\ \hline Lag = 4 & 59.2069 & 51.9591 & 47.1757 & 37.1099 & 27.4439 & 20.5398 \\ \hline Lag = 5 & 67.0300 & 57.1088 & 52.4225 & 44.5820 & 24.  
the AST2 FP-OHDBN approach has acquired a lower MSE value of 19.7508. Conversely, the LS-SVM system, GA-LSSVM approach, PSO-LSSVM method, FFO-LSSVM technique, and hybrid LSSVM models have resulted in ineffectual outcomes with higher MSE values of 67.0300, 57.1088, 52.4225, 44.5820, and 24.4347, correspondingly. A detailed equal coefficient (ECC) inspection of the results presented by the AST2 FP-OHDBN model with existing models is given in Figure 7. The results demonstrated that the AST2 FP-OHDBN model has attained enriched results, with higher values of ECC. For example, with lag = 1, the AST2 FP-OHDBN method has reached increased ECC of 0.9835. Conversely, the LS-SVM system, GA-LSSVM approach, PSO-LSSVM method, FFO-LSSVM technique, and hybrid LSSVM models have obtained decreased ECC of 0.9537, 0.9557, 0.9569, 0.9579, and 0.9792 respectively. Likewise, with lag = 5, the AST2 FP-OHDBN model has attained improved ECC of 0.9861. Conversely, the LS-SVM system, GA-LSSVM approach, PSO-LSSVM method, FFO-LSSVM technique, and hybrid LSSVM models have reached a reduced ECC of 0.9462, 0.9477, 0.9489, 0.9507, and 0.9724 respectively. Figure 6: MSE analysis of AST2 FP-OHDBN approach with existing methodologies. Figure 7: ECC analysis of AST2 FP-OHDBN approach with existing methodologies. Table 4 and Figure 8 depict a brief inspection of the AST2 FP-OHDBN model with existing methods in terms of running time (RT). The figure implicit in the AST2 FP-OHDBN model has acquired effectual outcomes over other models with minimal values of RT under all lags. For example, with lag = 1, the AST2 FP-OHDBN model has provided a reduced RT of 3.84 s, whereas the LS-SVM system, GA-LSSVM approach, PSO-LSSVM method, FFO-LSSVM technique, and hybrid LSSVM models have offered increased RT of 7.26 s, 19.53 s, 11.29 s, 14.25 s, and 9.80 s, correspondingly.