## 6 Conclusions and Future Work  
In this research, we utilized OBD and smartphone sensor data along with deep learning models to classify driving styles as aggressive or normal, and road traffic conditions as low, medium, or high. By utilizing OBD and smartphone sensor data instead of computer vision methods, we were able to implement prediction models in a more cost-effective manner. We experimented with various configurations of ANN models for class predictions, using SVM as a benchmark for comparison. The ANN architecture consisted of seven layers, and we trained three different configurations: the first without dropout, the second with dropout in the last three layers, and the third employing the bagging technique. In the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1 Score** \\ \hline The proposed ANN & 0.96 & 0.98 & 0.99 & 0.97 \\ The proposed ANN with dropout & 0.9 & 0.98 & 0.95 & 0.97 \\ The proposed ANN with bagging & 0.98 & 0.99 & 0.99 & 0.98 \\ SVM (Baseline) & 0.83 & 0.95 & 0.92 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Precision, recall, accuracy, and the F1 score values for road traffic condition prediction. Figure 7: Traffic road conditions prediction results. **(a)** The confusion matrix using the ANN model without dropout. **(b)** The confusion matrix using the ANN model with dropout. **(c)** The confusion matrix using ANN with bagging. **(d)** The confusion matrix using SVM. latter approach, the dataset was split to train five ANN models, and the final classifications were determined by averaging the predictions from all models. The dataset used for training the algorithms exhibited an imbalance, reflecting the inherent nature of driving styles and road traffic during data collection. This led to overfitting issues when training the ANN model, as it struggled to generalize predictions on validation data. To address this, the training dataset was balanced using a resampling approach. Despite this imbalance, the ANN models displayed superior accuracy and precision compared to the SVM model. However, the SVM model demonstrated higher recall values than the ANN model without dropout. The incorporation of dropout and bagging techniques notably improved the system's recall, particularly for the imbalanced low-labeled classes. Among the ANN models, the bagging approach consistently outperformed the others, as well as the SVM model, across all metrics, especially in terms of recall and F-score. This study underscores the superiority of ANN models over traditional machine learning techniques such as SVM. Figures 8 and 9 show the histograms for detection results for driving style and traffic conditions classifications. The utilization of OBD data and smartphone sensor data holds potential for addressing a wider array of road prediction challenges, including emissions, fuel consumption, and road infrastructure predictions such as identifying potholes. Incorporating data from vision sensors like cameras and radars could further enhance detection accuracy for these models. Moreover, more sophisticated deep learning methodologies leveraging time sequence data, such as recurrent neural networks (RNN) and Long Short-Term Memory (LSTM), could be employed with OBD and smartphone sensor data to tackle more complex prediction tasks. G.A.-r. developed the detection system architecture, defined the system elements, and designed the ANN models. He also implemented the training and testing models and contributed to the related work and conclusion. A.A. contributed to data analysis, sections structure, methodology, and results analysis. M.A.-r. contributed to the related work section, results, discussion, and conclusion. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.