LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations.

Traffic congestion, particularly in rapidly expanding urban centers, significantly impacts the timely delivery of emergency medical services (EMS), where every minute can mean the difference between life and death. Traditional traffic signal control systems often lack real-time adaptability to prioritize emergency vehicles, resulting in delays caused by congestion around ambulances. To address this critical issue, this paper presents an AI-driven real-time traffic management system designed to reduce EMS response times. The proposed solution incorporates three core components: Raspberry Pi-based traffic signal prioritization, deep learning-enabled audio-visual ambulance detection, and an advanced intelligent traffic management framework. For audio detection, raw data is transformed into spectrograms using Mel Frequency Cepstral Coefficients (MFCCs) and classified using a Long Short-Term Memory (LSTM) network. Visual data is processed through a ResNet18 convolutional neural network, pre-trained on ImageNet using inductive transfer learning. The outputs from the auditory and visual streams are integrated using empirical risk minimization, enabling accurate ambulance detection through multimodal data fusion. Performance evaluation demonstrates the effectiveness of the proposed system, achieving 98.3% accuracy in audio classification, 98.1% accuracy in visual classification, and 99% accuracy with the fused model. Additional metrics, including precision, recall, F1-score, and a confusion matrix, confirm the model's reliability. This innovative system has the potential to transform urban traffic networks into intelligent, adaptive systems, reducing delays caused by traffic congestion, enhancing emergency medical care response times, and ultimately saving lives. The framework offers a scalable blueprint for future smart city traffic management solutions, meticulously designed to support urban growth and expansion.