# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 1 Introduction
There are numerous types of traffic anomalies, spanning from minor infringements like illegal parking or pedestrian rule violations to severe incidents like accidents or reckless driving, which pose significant risks of harm and fatality to vehicle occupants and pedestrians alike. Given the critical nature of these traffic anomalies, considerable emphasis has been placed on their detection and prevention to mitigate driving-related hazards as much as possible. However, it is crucial to acknowledge that driving behavior is heavily influenced by individual drivers, making it challenging to regulate and control solely through external interventions. Thus, several systems are oriented to use these anomaly detections to manage traffic in areas of heavy traffic (both punctual and for long periods) as in the project [9]. Another peculiar case is its application in autonomous vehicles, as seen in the work [10], in which the state of the vehicle is checked from various sensors to identify unusual behaviors. Consequently, it is also necessary to use projects such as [11], in which vehicle sensor readings and the controller area network (CAN) communication protocol are used to make predictions of future messages to detect anomalies and ensure correct communication between vehicles to avoid possible safety failures. Among these, various methods have been used to obtain these events, such as the calculation of the optical flow of the objects in a sequence, the use of You Only Look Once (YOLO) [12] for detection and classification, pixel analysis, or more advanced technologies such as the application of trained neural networks (NNs) or generative models. The use of these techniques for the early detection of anomalies on the road is very important, since in cases of accidents or serious situations, the speed of action of the necessary services counts a lot for the successful resolution of the problem. In addition, it also facilitates the work of traffic management operators to identify problematic situations and act accordingly as quickly as possible or obtain much more information on the situation from these systems to ensure their correct resolution. In this way, it is possible to improve the quality of life both in urban areas and in the connections between them, as well as to increase the safety of drivers in the event of any anomalous situation. Another area of mobility where the application of these anomaly detection systems would be very beneficial is in the development of autonomous vehicles or interconnected vehicle systems, since this would provide greater safety in the use of these vehicles and improve their adaptation to irregular situations, always ensuring the health of users. To further contextualize these benefits, we present a comprehensive schematic in Figure 1 that outlines a common pipeline for traffic anomaly detection and how its integration could be implemented into a smart city framework. This diagram captures the end-to-end process--from data collection and preprocessing to detecting traffic anomalies and the system feedback--demonstrating how these models commonly function within real-time urban environments. It also illustrates how such systems can interact with broader smart infrastructure, including traffic control centers, emergency services, and autonomous vehicles, reinforcing the practical impact of these technologies in enhancing mobility safety, responsiveness, and adaptability. This review explores the evolution of traffic anomaly detection methods, with a particular focus on deep learning techniques. To guide the reader through the wide range of approaches, we introduce a clear taxonomy of models and methodologies in Figure 2, which categorizes them based on factors such as learning paradigm (supervised vs. unsupervised), architecture, and modality (single vs. multimodal). This structured overview supports a step-by-step analysis of each method, which is further developed in the subsequent sections.