# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 1 Introduction  
As cities grow and transportation networks become more complex, there is a growing need for systems that can ensure safety and smooth mobility. A key part of these systems is to detect and manage traffic anomalies: events that disrupt normal traffic patterns and create risks. These anomalies range from small issues like illegal parking or jaywalking to serious problems like accidents or reckless driving. Identifying and addressing these issues is crucial to keeping roads safe and traffic flowing smoothly. Recent advances in artificial intelligence, especially in video-based systems, have enabled real-time detection of traffic anomalies. These automated systems reduce the need for constant human supervision by quickly and accurately identifying unusual events. This not only reduces the workload for traffic managers but also improves the overall efficiency of urban transportation. As a result, cities become safer and more efficient places to live and travel. These systems play a crucial role in urban management by maintaining continuous vigilance over urban environments. They are designed to quickly identify and alert authorities to unusual events. This proactive approach not only facilitates the timely resolution of such events but also aids in their prevention, ensuring a safer urban environment. The utility of anomaly detection technologies extends beyond traffic and road safety, finding significant applications in sectors such as security, quality control in manufacturing, and patient health monitoring in medicine. Each of these applications shares a common goal of identifying irregular patterns to prevent and mitigate risks. * **Security:** Banks have been using AI-based techniques to detect anomalies in card transactions for years. As presented in [1; 2], a system is developed that learns to detect certain patterns of behavior common in fraud to block these actions and improve customers' security. * **Quality:** Due to the production and transportation of food and products, it has also been necessary to create systems such as in [3; 4] that capable of detecting anomalies in defective products for their correct disposal, as well as to check the quality of the products before their distribution and sale. * **Medicine:** Despite the great differences between the body of each patient, there are patterns of anomalies that are detectable by systems such as in [5; 6], which enable prevention and action by specialists in response to detected anomalies to ensure patient health. * **Sustainability:** From geographical data, it is possible to use some applications such as in [7; 8] to detect polluting elements or areas with high concentrations of pollutants, which enable the implementation of measures for their reduction. Another major field for the application of automatic anomaly detection systems is mobility. With the extensive connectivity among urban centers such as cities, towns, and villages, there exists a substantial volume of traffic moving between these locations, leading to increased chances of abnormal incidents occurring on roads. These incidents can impede traffic flow and impact transportation, a crucial sector. Within mobility, traffic anomalies encompass various scenarios that disrupt normal traffic flow and endanger road users' safety. These anomalies range from minor infractions like illegally parked vehicles or jaywalking pedestrians to more severe situations such as speeding, erratic driving, or major accidents. Each type of anomaly presents distinct challenges and risks, underscoring the importance of their early detection and management for ensuring safety and efficiency in urban transportation systems. Prompt identification and response to these anomalies are essential for enhancing road safety and urban quality of life.