## 4 Results and Discussion
### Features Classification Results  
The main goal of this study is to evaluate how well the trained models, as described earlier, classify various crash-related features using the pedestrian crash diagrams database. The subsequent results contain the classification results for six binary features. These features include the presence of a Michigan left turn (yes or no), the presence of an adjacent residential area (yes or no), the type of intersection (three-leg or four-leg), the type of road (divided or undivided), the type of crosswalk (marked or unmarked), and the intersection angle (skewed or unskewed). Notably, the comparison results from AlexNet, VGG-19, and ResNet-50, which underline the thoroughness of this study, are included. The training model was implemented by generating the steps per epoch. Processing the entire dataset once with the neural network constituted one epoch. The total number of epochs allocated for the training was 50. Table 4a displays the training accuracy and F-score outcomes for the AlexNet, VGG-19, and ResNet-50 CNN models. Meanwhile, Table 4b shows the validation accuracy and F-score results used to assess the models' performance. The model's prediction results are compared to the ground truth at this stage. Confusion matrices were developed to calculate each validation model's mean accuracy and F-score values. The \"Mean\" rows in both tables display the average accuracy and F-score values for all folds of each architecture, enabling us to assess the overall performance of each CNN model. This aids in the selection of the best one on average for each feature. Notably, the comparison of the various CNN models for each specific feature is made easier by higher accuracy and F-score values, which indicate superior performance in categorizing the crash diagrams. The classification results for each feature indicate that the AlexNet CNN model surpasses other models, attaining the highest average training and validation accuracy and F-score values over 50 epochs. Specifically, the AlexNet model consistently provides superior performance metrics, indicating its robustness and reliability in feature recognition tasks. In comparison, the ResNet-50 model exhibits exceptional performance in classifying all features except the crosswalk type feature, where AlexNet outperforms it but still surpasses the VGG-19 model. These findings underscore the efficacy of the AlexNet and ResNet-50 models in pedestrian crash diagram classification. Moreover, they suggest the potential applications of these models in traffic safety analysis and automated feature extraction, opening up new avenues for research and development. Our findings, which align with existing literature on the effectiveness of AlexNet in image classification tasks, are particularly noteworthy. For instance, [PERSON] et al. demonstrated AlexNet's exceptional performance in ILSVRC-12, where it not only achieved top accuracy, but also set new benchmarks in object recognition [57]. This foundational work underscored AlexNet's ability to handle large datasets and complex image classification tasks effectively, leaving a lasting impression on the field. Our study's results in classifying features within pedestrian crash diagrams further validate this finding. In more recent applications, AlexNet has been employed in medical image analysis, remote sensing, and other fields requiring precise feature extraction from complex imagery. For example, in the study by [PERSON] et al., AlexNet was adapted for lung pattern classification in Computed Tomography (CT) images, achieving remarkable accuracy and demonstrating its versatility and robustness [58]. Similarly, our study benefits from AlexNet's architecture, which efficiently captures and differentiates intricate details in pedestrian crash diagrams. Interestingly, the ResNet-50 model's superior performance, as also noted by [59], is a testament to the potential utility of deeper networks in capturing intricate details in images. This advantage over VGG-19 significantly enhances classification accuracy.