Deep Learning-Based Traffic Management for Emergency Vehicles Using Convolutional Neural Networks

Traffic congestion significantly impairs the prompt movement of emergency vehicles in densely populated urban areas, frequently resulting in significant delays To address this challenge, we propose an Automatic Traffic Controller for Emergency Vehicles (ATCEV) based on deep learning algorithms and Radio Frequency Identification (RFID) technology. Specifically, a Convolutional Neural Network (CNN) is employed to predict traffic congestion levels and dynamically optimize signal control. RFID readers installed at key junctions detect the unique identifiers of approaching emergency vehicles. The deep learning model then analyzes real-time traffic conditions, learns traffic flow patterns, and adjusts signal timings to prioritize the passage of emergency vehicles. The system minimizes disruption to overall traffic flow while guaranteeing emergency responders an immediate right-way by combining RFID detection with CNN-based predictive analytics. This approach introduces an adaptive, scalable, and intelligent solution to modern urban traffic management, significantly enhancing the efficiency and reliability of emergency response operations.