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Human behavior significantly contributes to severe road injuries, underscoring a critical road safety challenge. This study addresses the complex task of predicting dangerous driving behaviors through a comprehensive analysis of over 356,000 trips, enhancing existing knowledge in the field and promoting sustainability and road safety. The research...
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... ROC curves presented in Figure 7 further illustrate the performance of the bestperforming models-Gradient Boosting (GB) and Multilayer Perceptron (MLP). The ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates The ROC curves presented in Figure 7 further illustrate the performance of the bestperforming models-Gradient Boosting (GB) and Multilayer Perceptron (MLP). ...
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... ROC curves presented in Figure 7 further illustrate the performance of the bestperforming models-Gradient Boosting (GB) and Multilayer Perceptron (MLP). The ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates The ROC curves presented in Figure 7 further illustrate the performance of the bestperforming models-Gradient Boosting (GB) and Multilayer Perceptron (MLP). The ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates high overall performance. ...
Context 3
... ROC curves presented in Figure 7 further illustrate the performance of the bestperforming models-Gradient Boosting (GB) and Multilayer Perceptron (MLP). The ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates The ROC curves presented in Figure 7 further illustrate the performance of the bestperforming models-Gradient Boosting (GB) and Multilayer Perceptron (MLP). The ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates high overall performance. ...
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... ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates The ROC curves presented in Figure 7 further illustrate the performance of the bestperforming models-Gradient Boosting (GB) and Multilayer Perceptron (MLP). The ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates high overall performance. Similarly, the ROC curve for MLP (Figure 7b) shows comparable effectiveness, underscoring the robustness of these models in capturing true positive rates while maintaining low false positive rates. ...
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... ROC curve for GB (Figure 7a) demonstrates a strong ability to distinguish between dangerous and non-dangerous driving behaviors, with an area under the curve (AUC) that indicates high overall performance. Similarly, the ROC curve for MLP (Figure 7b) shows comparable effectiveness, underscoring the robustness of these models in capturing true positive rates while maintaining low false positive rates. Sustainability 2024, 16, 6151 13 of 19 high overall performance. ...
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... 2024, 16, 6151 13 of 19 high overall performance. Similarly, the ROC curve for MLP (Figure 7b) shows comparable effectiveness, underscoring the robustness of these models in capturing true positive rates while maintaining low false positive rates. Overall, the models performed adequately in terms of predictive ability and at relatively similar performance levels compared to each other. ...
