Traffic Incident Detection

Traffic incident detection aims to automatically identify and characterize traffic disruptions using various sensor data, improving traffic flow and safety. Current research emphasizes developing robust models, including deep learning architectures like convolutional neural networks and transformers, often combined with generative adversarial networks to address data scarcity and imbalance issues. Furthermore, research explores both centralized and decentralized approaches, leveraging techniques like federated learning and network lasso to handle distributed data sources and improve efficiency. These advancements hold significant potential for enhancing intelligent transportation systems and improving real-time traffic management.