# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 2 Algorithms and Methods
### Convolutional Neural Networks (CNNs)
Component-wise analysis highlighted the individual contributions of each element, emphasizing their combined effectiveness. Exploratory data analysis revealed significant temporal patterns, such as the impact of speed limits and seasonal variations, underscoring the importance of spatial and temporal factors in accident severity prediction. This approach has broad implications for road safety. It enables the identification of risk factors, supports preventive interventions, and serves as an early warning system for emergency teams, improving response effectiveness. Future research will focus on validating the model across different regions and incorporating additional data sources, such as social networks and sensors, to further enhance its accuracy and applicability. Expanding on unsupervised approaches for anomaly detection, this study [36] leverages deep autoencoders to identify anomalies in surveillance videos. Autoencoders encode input data into a compressed latent space and reconstruct it, learning normal patterns during training. Significant reconstruction errors in test frames signal anomalies, as unusual events deviate from the learned patterns. The proposed model incorporates the following:  
* Conv3D layers: Extract spatial and temporal features from video sequences. * ConvLSTM2D layers: Combine CNN and LSTM capabilities for improved spatio-temporal processing. * ConvTranspose3D layers: Enhance spatial resolution for better reconstruction accuracy. To identify anomalies, a thresholding mechanism combines reconstruction error with kernel density estimation (KDE) in the intermediate latent space, enabling more precise anomaly detection. The model was evaluated on the UCSD Peds1 [37] and CUHK Av  
Figure 9: The structure of the proposed model for predicting the level of accident severity. From [35]. enue [38] datasets, achieving an AUC of 86.4% and 88.9%, respectively, demonstrating strong performance compared to current methods. Another approach based on incorporating machine learning into anomaly detection was introduced in [39]. In which a Siamese CNN was proposed to learn a distance function between pairs of video patches. Unlike methods using predefined distance functions, this approach optimizes feature vectors and distance metrics through training, enabling robust anomaly localization across diverse scenes. The system shown in Figure 10 employs an exemplar-based nearest neighbor method to model normal activity with feature vectors (exemplars) for each spatial region. Test patches are compared to these exemplars, and patches significantly differing from all exemplars are flagged as anomalous. The Siamese CNN architecture includes twin convolutional branches with shared weights, processing similar and dissimilar pairs, and a classification pipeline minimizing cross-entropy loss. The method was evaluated on the UCSD Ped1, Ped2 [37], and CUHK Avenue [38] datasets, outperforming or matching state-of-the-art methods. New evaluation criteria based on regions and tracking further improved the practical relevance of the results.