# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 2 Algorithms and Methods
### Convolutional Neural Networks (CNNs)
Addressing the need for efficient anomaly detection systems, in [32], Light-WVAD was introduced: a lightweight and effective model based on weakly supervised learning. Unlike fully supervised methods, Light-WVAD requires only video-level labels, specifying whether a video contains anomalies without frame-level annotations. The model comprises three innovative modules (Figure 8):  
* Multilevel temporal correlation attention module (MTA): Captures temporal relationships between video clips of varying duration. * Hourglass fully connected layer (HFC): Reduces parameters by half compared to conventional layers, maintaining performance while improving efficiency. * Adaptive instance selection strategy (AIS): Dynamically selects reliable instances with the highest anomaly scores for loss computation, addressing the uncertainty of weakly labeled data. To enhance optimization, an antagonistic loss function is employed, ensuring normal instances score near zero while anomalous ones score near one. Experimental results on the UCF-Crime [17] and ShanghaiTech [33] datasets demonstrated the model's exceptional performance. Light-WVAD achieved an AUC of 95.9% on ShanghaiTech, outperforming most conventional methods with only 0.14 million parameters. On UCF-Crime, it achieved an AUC of 84.7%, ranking highest among weakly supervised lightweight models and third among all models. Another work, Ref. [34], introduced a hybrid model for road accident detection using CCTV images. Based on EfficientNet-B7 as the core architecture, the system demonstrates both high accuracy and efficiency in identifying accidents. The model development encompasses three key components:  
* Data preparation: The Accident Detection From CCTV Footage dataset, consisting of 990 balanced images of accidents and non-accidents, is used. Preprocessing techniques such as data augmentation, normalization, and resizing are applied to optimize training. Model architecture: EfficientNet-B7 is combined with additional Conv2D, Flatten, and Dense layers to create a hybrid model that extracts spatial features effectively, ensuring high performance. Training and evaluation: The model achieves a training accuracy of 99.24%, a cross-validation accuracy of 94.98%, and an area under the curve (AUC) of 1.00, confirming its robustness in distinguishing accidents from non-accidents. The model significantly impacts road safety by enabling real-time accident detection and facilitating timely interventions. Furthermore, its high interpretability enhances trust and transparency in its predictions. Future research is proposed to explore multimodal data integration, real-time connections with traffic management systems, and further improvements in model interpretability. Figure 8: Overview of the Light-WVAD framework for weakly supervised video anomaly detection. The model uses a multiple instance learning (MIL) setup, where videos are split into 32 segments and grouped into positive (abnormal) or negative (normal) bags. Features are extracted using a pre-trained I3D network and processed by a multi-level temporal attention (MTA) module to capture time-based patterns. These are then passed through a fully connected layer to generate anomaly scores. An adaptive instance selection (AIS) method picks the most informative segments from each bag to calculate the final loss. From [32]. In [35], a deep learning model to predict traffic accident severity was presented. By addressing challenges in feature extraction from complex data and capturing temporal dependencies, the model integrates a one-dimensional convolutional neural network (1D CNN), a bidirectional long short-term memory network (BiLSTM), and attentional mechanisms. This architecture effectively extracts spatial features, captures temporal relationships, and assigns feature importance to optimize prediction accuracy (Figure 9). The proposed model outperformed five standard methods--SVM, XGBoost, CNN 1D, DNN, and CNN-LSTM--in accuracy, recall, and F1 score.