## 5 Conclusions  
In this article, a novel AST2 FP-OHDBN model was projected for traffic flow prediction in smart city environments. The presented AST2 FP-OHDBN model follows a three-stage process: min-max normalization, HDBN-based traffic flow forecasting, and POA-based hyperparameter tuning. Since the trial-and-error hyperparameter tuning of the HDBN  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Running Time (sec)**} \\ \hline
**No. of** & **LS-SVM** & **Genetic** & **Algorithm-** & **Particle Swarm** & **FruitFly Opt.** & **Hybrid-** & **AST2 FP-** \\
**Lags** & **Model** & **LSSVM** & **Opt-LSSVM** & **LSSVM** & **LSSVM** & **OHDBN** \\ \hline Lag = 1 & 7.26 & 19.53 & 11.29 & 14.25 & 9.80 & 3.84 \\ \hline Lag = 2 & 7.30 & 20.01 & 11.64 & 14.45 & 9.98 & 3.56 \\ \hline Lag = 3 & 7.42 & 20.33 & 11.67 & 15.06 & 10.02 & 5.13 \\ \hline Lag = 4 & 7.78 & 20.52 & 12.18 & 15.28 & 10.03 & 5.05 \\ \hline Lag = 5 & 7.89 & 22.00 & 12.24 & 15.31 & 10.44 & 5.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RT analysis of AST2 FP-OHDBN approach with existing methodologies.  
Figure 8: RT analysis of AST2 FP-OHDBN approach with existing methodologies.  
model is a tedious process, the POA is applied as a hyperparameter optimizer, which considerable enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2 FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2 FP-OHDBN model over recent state-of-the-art DL models with minimal average MSE of 17.19132 and RMSE of 22.6634. Therefore, the AST2 FP-OHDBN algorithm can be employed to accomplish high-precision traffic prediction in the near future prediction on smart cities environment. In future, hybrid metaheuristics can be designed to enhance prediction outcomes.  
Conceptualization, H.A. and S.S.A.; methodology, N.A. ([PERSON]); software, G.P.M.; validation, N.A. ([PERSON]), H.A. and H.M.; formal analysis, H.M.; investigation, N.A. ([PERSON]); resources, H.M.; data curation, G.P.M.; writing--original draft preparation, H.A., S.S.A. and N.A. ([PERSON]); writing--review and editing, N.A. ([PERSON]); visualization, H.M.; supervision, S.S.A.; project administration, G.P.M.; funding acquisition, H.A., N.A. ([PERSON]) and S.S.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through General Research Project under grant number (40/43). [PERSON] [PERSON] hint Abdulrahman University Researchers Supporting Project number (PNURSP2022R303), [PERSON] hint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22 UQU4210118 DSR37).  
This article does not contain any studies with human participants performed by any of the authors.  
Not applicable.  
Data Availability StatementData sharing not applicable to this article as no datasets were generated during the current study.  
The authors declare that they have no conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.