## 4 Results and Discussion  
This section inspects the prediction performance of the AST2 FP-OHDBN model in distinct aspects. The proposed model is tested by the traffic data containing all 30 s raw sensor data for a duration of 30 days. The traffic data collected during the first 10 days are used as a training set and the remaining 20 days data are utilized as testing set. In this experiment, data groups consist of the 15 min of aggregated data in vehicles per 15 min (veh per 15 min). Thereby, 96 data groups are available for each day. Before the calculation, the data groups are normalized (as given in Section 3.1), rendering the data in the range of 0 to 1. Firstly, the mean absolute percentage error (MAPE) analysis of the proposed model is performed. The MAPE is a widely utilized measure for prediction process. It defines the ration of the sum of the individual absolute errors to the demand (each period separately). Table 1 and Figure 3 offer a detailed fitness value examination of the AST2 FP-OHDBN model under varying numbers of iterations. The experimental values implicit in the AST2 FP-OHDBN method have shown effectual outcomes with minimal fitness values. For instance, with 10 iterations, the AST2 FP-OHDBN model has offered best, average, and worst fitness values of 1.996%, 5.237%, and 8.795% respectively. Meanwhile, with 50 iterations, the AST2 FP-OHDBN method has provided best, average, and worst fitness values of 0.471%, 1.678%, and 5.046%, correspondingly. Eventually, with 100 iterations, the AST2 FP-OHDBN technique has offered best, average, and worst fitness values of 0.471%, 1.013%, and 3.421%, correspondingly. Table 2 and Figure 4 provide a detailed prediction results study of the AST2 FP-OHDBN model under varying index. The experimental values highlighted the AST2 FP-OHDBN method have attained closer predicted values under each run. For instance, on run-1 with an actual value of 190, the AST2 FP-OHDBN model has attained the predicted value of 226. Moreover, on run-2 with an actual value of 190, the AST2 FP-OHDBN method has reached the predicted value of 225. Furthermore, on run-3 with an actual value of 190, the AST2 FP-OHDBN technique has gained the predicted value of 247. Finally, on run-5 with an actual value of 190, the AST2 FP-OHDBN approach has reached the predicted value of 268. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**MAPE (\%)**} \\ \hline
**No. of Iterations** & **Best Fitness** & **Average Fitness** & **Worst Fitness** \\ \hline
0 & 10.828 & 11.051 & 10.987 \\ \hline
10 & 1.996 & 5.237 & 8.795 \\ \hline
20 & 0.884 & 3.140 & 7.302 \\ \hline
30 & 0.471 & 1.075 & 5.046 \\ \hline
40 & 0.471 & 1.583 & 4.506 \\ \hline
50 & 0.471 & 1.678 & 5.046 \\ \hline
60 & 0.471 Table 3 demonstrates an extensive comparative study of the AST2 FP-OHDBN model, with recent models given in terms of different measures [23]. Figure 5 illustrates a comparative scrutiny of the AST2 FP-OHDBN method with existing methods in terms of RMSE. The figure represented the AST2 FP-OHDBN technique has gained effectual outcomes over other models with minimal values of RMSE under all lags. For example, with lag = 1, the AST2 FP-OHDBN technique has offered reduced RMSE of 26.9257. Conversely, the LS-SVM system, GA-LSSVM approach, PSO-LSSVM method, FFO-LSSVM technique, and hybrid LSSVM models have offered increased RMSE of 53.8171, 42.6758, 39.5084, 38.0203, and 32.7534 respectively. At the same time, with lag = 5, the AST2 FP-OHDBN model has gained lower RMSE value of 23.4312. 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 RMSE values of 98.8703, 58.9394, 55.5521, 53.4518, and 28.7079 respectively.