# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 7 Conclusions  
In this study, we described the \"Road Scanner\" route application, which enables onboard users to distinguish between four different types of road segments: smooth, bumpy, pothodel, and others. The application uses the accelerometer, gyroscope, and GPS integrated into smartphones to record multimodal data for each tagged segment. A dataset was gathered and combined with other online datasets to serve as a comparison for the classification methods in order to evaluate the application. In fact, a number of machine learning models were used to map the sensors-data that were gathered to the appropriate route state. Python and well-known data science tools were used to implement all models and evaluation criteria. SVM and Random Forest were the top two classifiers. The Extra Trees classifier and the Random Forest classifier produced excellent outcomes for the AUC metric. Additionally, all measures and all classifiers showed low standard deviation rates. Overall, the high average rates and the low standard deviation rates (across all metrics) attest to the chosen classifiers strong performance. Future work will involve increasing the sampling rate when recording the pothodel segments in order to address the speed dependency issue that was previously mentioned. Additionally, the future edition will be enhanced by machine learning models that are deployed online to provide real-time notifications regarding various road surface conditions, which represents a challenge because of the problem of sensor qualities and precision \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Training Time** & **Testing Time** \\ \hline ANN & 6.024 & 0.002 \\ SVM & 1.066 & 0.073 \\ Random Forest & 1.737 & 0.055 \\ Extra Trees & 0.7 & 0.059 \\ Bagging & 46.805 & 5.776 \\ Gradient Boosting & 3.796 & 0.004 \\ Stacking & 5.13 & 0.101 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Execution times (training and testing) for all classifiers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F-Score** \\ \hline Smooth & 90.07 & 93.24 & 91.63 \\ Pothodel & 82.70 & 75.87 & 79.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results per class for the SVM classifier. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **AUC (Standard Deviation)** \\ \hline ANN & 91.44 (\(\pm\)1.93) \\ SVM & 91.83 (\(\pm\)1.60) \\ Random Forest & 93.89 (\(\pm\)0.22) \\ Extra Trees & 94.94 (\(\pm\)1.21) \\ Bagging & 92.13 (\(\pm\)1.21) \\ Gradient Boosting & 92.43 (\(\pm\)0.78) \\ Stacking & 93.12 (\(\pm\)0.49) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUC results for all classifiers. that varies depending on a smartphones brand and specifications; a challenge that we are willing to face by developing appropriate noise filters for use with different smartphones sensor specifications. Furthermore, we are willing to include the actual excluded classes 'bumps' and 'other' by focusing on road areas that will permit us to record sufficient data. We will also attempt to use formal verification techniques to validate the different steps of the proposed approach [40]. The security challenges related to the use of our application need to be considered as well [41; 42]. Our application may be also extended in order to cover other aspects and needs in our modern smart societies, cities, and homes [43; 44]. [PERSON] conceived the presented approach, worked on computations, and participated in the redaction of many parts of the paper. [PERSON] participated in the development of the approach and conducted the literature review. [PERSON] worked on the results section as well as the discussion and interpretation. [PERSON] worked on the theoretical part of the experiment. [PERSON] participated in the application development for data collection. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.