# Utilizing Different Machine Learning Techniques to Examine Speeding Violations
## 4 Conclusions  
This research aimed to analyze speeding violations in the U.S. using machine learning techniques, including CART, RF, and MLP. A total of 51,136 data records from the FARS were analyzed using WEKA software. Data related to drivers, the environment, vehicles, roads, and accident time were studied. The data were cleaned and balanced using the SMOTE technique. The accuracy, RMSE, F-measure, Kappa statistic, AUC, and ROC evaluation metrics were used to check the performance of the algorithms. The outcome of this research concluded that young drivers tend to underestimate speeding risks and overestimate their driving abilities. Speeding violations were more common in the age groups (25) and (26-35); the percentages of violators in these two groups were 30.7% and 25.7%, respectively. Bad weather conditions were also associated with a high percentage of speeding violations. Slippery roads due to rain, snow, or ice at low temperatures increase braking distance, which raises speeding violations. The speed limit was a significant factor affecting speeding violations. Different age groups violate speed limits at different speeds (30-40, 40-50, 50-60, 60-70, 70-80, and 80-90 km/h). These speed limits may not be appropriate for traffic streams. Another influencing factor in this study was road alignment. The results showed that speeding violations increase at curves. Different factors at curves affect vehicles' speeds, such as the radius of the horizontal curve and the sight distance at vertical curves. Furthermore, speeding violations were affected by accident time, whereby night periods experienced high speeding violations. The analysis results indicated that the three classifiers were capable of classifying and predicting speeding violations. The RF was the best method for analyzing and predicting speeding violations. Based on the results of this study, it is recommended to raise the awareness of drivers, especially the young ones, about the dangers of excessive speed, in general, and its dangers under bad weather conditions. In addition, it is urged to apply the law firmly and fairly and increase police enforcement. This work focused on studying speeding violations, in general, using CART, RF, and MLP. For further studies, it is recommended to use different ML algorithms and utilize other programs or programming languages for studying speeding violations. Furthermore, speeding violations can be studied for more specific cases, such as speeding violations for vehicle type (i.e., passenger cars or trucks), area type (i.e., rural, or urban), or any other specific factor. Speeding violations should also be studied with traditional statistical and machine learning methods. Then, compare how well each method can predict speeding violations. Conceptualization, A.H.A. and T.K.A. Methodology, A.H.A., B.W.A.-M. and T.K.A. Software, T.K.A. and M.S.O. Validation, A.H.A., T.K.A. and M.S.O. Formal analysis, A.H.A., T.K.A. and M.S.O. Investigation, A.H.A. and T.K.A. Resources, A.H.A. and B.W.A.-M. Data curation, T.K.A. and M.S.O. Writing--original draft preparation, A.H.A. and T.K.A. Writing--review and editing, A.H.A., B.W.A.-M., T.K.A. and M.S.O. Visualization, A.H.A. and T.K.A. Supervision, A.H.A. and B.W.A.-M. Project administration, A.H.A. and B.W.A.-M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This study obtained data from the Fatality Analysis Reporting System (FARS) for the years 2015-2019. The National Highway Traffic Safety Administration (NHTSA) is responsible for maintaining this database system. Available at: [[https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars](https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars)]([https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars](https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars)) (accessed on 10 January 2023). The authors declare no conflict of interest.