## 4 Results and Discussion
### Limitations of the Study  
While this study demonstrates the potential use of CNNs in pedestrian crash diagram classification, certain limitations must be acknowledged. These limitations highlight areas for improvement and future research opportunities.  
One key limitation is the dataset's size and generalizability. The study utilizes pedestrian crash diagrams from Michigan UD-10 crash reports, covering data from 2020-2023. While this dataset is representative of pedestrian crashes in Michigan, its size and geographic scope may limit the generalizability of findings to other states or countries with different crash reporting formats. Future studies could benefit from incorporating larger or multi-regional datasets to improve model robustness and transferability.  
Another limitation concerns model complexity and computational constraints. The CNN architectures used in this study--AlexNet, VGG-19, and ResNet-50--are well-established but computationally intensive, requiring high-performance GPUs for training and inference. The computational cost may pose challenges for agencies with limited resources or for real-time applications requiring the immediate classification of crash diagrams. Future research could explore lighter, more efficient deep learning models or transfer learning approaches to reduce computational demands.  
Feature selection and interpretability also present challenges in this study. The research focuses on automated feature extraction from crash diagrams, classifying attributes such as intersection type, road type, intersection angle, and the presence of crosswalks, Michigan left turns, or nearby residential areas. While CNNs learn patterns from images effectively, their lack of explainability remains challenging. Unlike traditional statistical models, CNNs operate as black-box models, making it difficult to interpret individual feature importance. Future work could integrate Explainable AI (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM) or Shapley Additive Explanations (SHAP), to enhance model transparency and improve the interpretability of feature extraction.  
Potential bias in data annotation is another factor that may influence the study's outcomes. The model's accuracy depends on the quality of the labeled crash diagrams. If any mis-annotations or inconsistencies exist in the dataset, they may impact classification performance. Future research could explore semi-supervised or unsupervised learning approaches to reduce reliance on manually labeled data.  
Finally, exploring advanced architectures presents an opportunity for further research. While this study establishes a baseline using CNN architectures, newer deep learning models--such as Vision Transformers and hybrid CNN-Transformer architectures--have shown promising results in image classification. Future studies could explore these models to assess whether they improve accuracy and computational efficiency in crash diagram classification.  
Despite these limitations, this study provides an essential first step in automating pedestrian crash diagram classification using CNNs. Addressing these challenges in future research could further enhance deep learning models' accuracy, efficiency, and real-world applicability in transportation safety analysis.